id stringlengths 2 115 | lastModified stringlengths 24 24 | tags list | author stringlengths 2 42 ⌀ | description stringlengths 0 68.7k ⌀ | citation stringlengths 0 10.7k ⌀ | cardData null | likes int64 0 3.55k | downloads int64 0 10.1M | card stringlengths 0 1.01M |
|---|---|---|---|---|---|---|---|---|---|
Francesco/thermal-dogs-and-people-x6ejw | 2023-03-30T09:19:15.000Z | [
"task_categories:object-detection",
"annotations_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:cc",
"rf100",
"region:us"
] | Francesco | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: image_id
dtype: int64
- name: image
dtype: image
- name: width
dtype: int32
- name: height
dtype: int32
- name: objects
sequence:
- name: id
dtype: int64
- name: area
dtype: int64
- name: bbox
sequence: float32
length: 4
- name: category
dtype:
class_label:
names:
'0': thermal-dogs-n-people
'1': dog
'2': person
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- en
license:
- cc
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- object-detection
task_ids: []
pretty_name: thermal-dogs-and-people-x6ejw
tags:
- rf100
---
# Dataset Card for thermal-dogs-and-people-x6ejw
** The original COCO dataset is stored at `dataset.tar.gz`**
## Dataset Description
- **Homepage:** https://universe.roboflow.com/object-detection/thermal-dogs-and-people-x6ejw
- **Point of Contact:** francesco.zuppichini@gmail.com
### Dataset Summary
thermal-dogs-and-people-x6ejw
### Supported Tasks and Leaderboards
- `object-detection`: The dataset can be used to train a model for Object Detection.
### Languages
English
## Dataset Structure
### Data Instances
A data point comprises an image and its object annotations.
```
{
'image_id': 15,
'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x640 at 0x2373B065C18>,
'width': 964043,
'height': 640,
'objects': {
'id': [114, 115, 116, 117],
'area': [3796, 1596, 152768, 81002],
'bbox': [
[302.0, 109.0, 73.0, 52.0],
[810.0, 100.0, 57.0, 28.0],
[160.0, 31.0, 248.0, 616.0],
[741.0, 68.0, 202.0, 401.0]
],
'category': [4, 4, 0, 0]
}
}
```
### Data Fields
- `image`: the image id
- `image`: `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`
- `width`: the image width
- `height`: the image height
- `objects`: a dictionary containing bounding box metadata for the objects present on the image
- `id`: the annotation id
- `area`: the area of the bounding box
- `bbox`: the object's bounding box (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format)
- `category`: the object's category.
#### Who are the annotators?
Annotators are Roboflow users
## Additional Information
### Licensing Information
See original homepage https://universe.roboflow.com/object-detection/thermal-dogs-and-people-x6ejw
### Citation Information
```
@misc{ thermal-dogs-and-people-x6ejw,
title = { thermal dogs and people x6ejw Dataset },
type = { Open Source Dataset },
author = { Roboflow 100 },
howpublished = { \url{ https://universe.roboflow.com/object-detection/thermal-dogs-and-people-x6ejw } },
url = { https://universe.roboflow.com/object-detection/thermal-dogs-and-people-x6ejw },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2022 },
month = { nov },
note = { visited on 2023-03-29 },
}"
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
magicgh/alpaca-cleaned | 2023-04-10T07:48:32.000Z | [
"license:cc-by-4.0",
"region:us"
] | magicgh | null | null | null | 1 | 187 | ---
license: cc-by-4.0
---
|
tasksource/icl-symbol-tuning-instruct | 2023-07-26T07:20:41.000Z | [
"task_categories:text2text-generation",
"task_categories:text-classification",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:apache-2.0",
"in-context-learning",
"symbol-tuning",
"icl",
"meta-icl",
"meta-learning",
"flan",
"long-input",
"instruction-tuning",
"instruct",
"metaicl",
"arxiv:2110.15943",
"arxiv:2305.08298",
"arxiv:2301.05948",
"region:us"
] | tasksource | null | null | null | 10 | 187 | ---
license: apache-2.0
task_categories:
- text2text-generation
- text-classification
- text-generation
language:
- en
tags:
- in-context-learning
- symbol-tuning
- icl
- meta-icl
- meta-learning
- flan
- long-input
- instruction-tuning
- instruct
- metaicl
dataset_info:
features:
- name: task
dtype: string
- name: inputs
dtype: string
- name: targets
dtype: string
- name: symbols
sequence: string
splits:
- name: validation
num_bytes: 42218685.0
num_examples: 14970
- name: test
num_bytes: 43453364.0
num_examples: 16204
- name: train
num_bytes: 1303015298.0
num_examples: 452367
download_size: 727062369
dataset_size: 1388687347.0
size_categories:
- 100K<n<1M
---
# Description
Few-shot prompting demonstrates that language models can learn in context even though they were not trained to do. However, explicitly learning to learn in context [meta-icl](https://arxiv.org/abs/2110.15943) leads to better results. With symbol tuning, labels are replaced with arbitrary symbols (e.g. foo/bar), which makes learning in context a key condition to learn the instructions
We implement *symbol tuning*, as presented in the [Symbol tuning improves in-context learning](https://arxiv.org/pdf/2305.08298.pdf) paper with tasksource classification datasets.
An input is a shuffled sequence of 4 positive and 4 negative examples showing a particular label (replaced with a symbol - a random word), followed by an example to label.
This is the largest symbol-tuning dataset to date, with 279 datasets. Symbol tuning improves in-context learning, which tends to be degraded by instruction tuning.
# Usage
We limit input size to 50_000 characters. This is well enough to challenge long range modeling. But be careful to remove examples that are too long or to truncate from left, otherwise some examples might be unsolvable, as the "question" are at the end of the examples.
```python
dataset = load_dataset('tasksource/icl-symbol-tuning-instruct')
# assuming 4 characters per token and 1000 tokens
dataset = dataset.filter(lambda x:len(x['inputs'])<1000*4)
```
## References:
Code: https://github.com/sileod/tasksource
```
@article{sileo2023tasksource,
title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation},
author={Sileo, Damien},
url= {https://arxiv.org/abs/2301.05948},
journal={arXiv preprint arXiv:2301.05948},
year={2023}
}
@article{wei2023symbol,
title={Symbol tuning improves in-context learning in language models},
author={Wei, Jerry and Hou, Le and Lampinen, Andrew and Chen, Xiangning and Huang, Da and Tay, Yi and Chen, Xinyun and Lu, Yifeng and Zhou, Denny and Ma, Tengyu and others},
journal={arXiv preprint arXiv:2305.08298},
year={2023}
}
``` |
yzhuang/autotree_automl_10000_credit_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T02:24:10.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 126879367
dataset_size: 472880000
---
# Dataset Card for "autotree_automl_10000_credit_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_pmlb_10000_phoneme_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T04:06:07.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 185240000
num_examples: 10000
- name: validation
num_bytes: 185240000
num_examples: 10000
download_size: 68514231
dataset_size: 370480000
---
# Dataset Card for "autotree_pmlb_10000_phoneme_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_automl_10000_MagicTelescope_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T05:48:36.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 186721409
dataset_size: 472880000
---
# Dataset Card for "autotree_automl_10000_MagicTelescope_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_automl_10000_MiniBooNE_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T06:03:38.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 293033260
dataset_size: 472880000
---
# Dataset Card for "autotree_automl_10000_MiniBooNE_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_automl_10000_jannis_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T06:07:04.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 292435235
dataset_size: 472880000
---
# Dataset Card for "autotree_automl_10000_jannis_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_pmlb_10000_twonorm_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T06:47:08.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 144253019
dataset_size: 472880000
---
# Dataset Card for "autotree_pmlb_10000_twonorm_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_automl_10000_heloc_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T07:33:56.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 187 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 80234603
dataset_size: 472880000
---
# Dataset Card for "autotree_automl_10000_heloc_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
pavlichenko/WizardLM_evol_instruct_70k_train_val_split | 2023-09-16T12:26:29.000Z | [
"task_categories:conversational",
"size_categories:10K<n<100K",
"region:us"
] | pavlichenko | WizardLM dataset splitted in train and validation. | null | null | 0 | 187 | ---
task_categories:
- conversational
size_categories:
- 10K<n<100K
--- |
maritaca-ai/imdb_pt | 2023-04-01T16:15:34.000Z | [
"region:us"
] | maritaca-ai | Large Movie Review Dataset.
This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.\ | @InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
} | null | 2 | 186 | Entry not found |
nanyy1025/covid_fake_news | 2023-02-24T01:36:24.000Z | [
"task_categories:text-classification",
"task_categories:zero-shot-classification",
"language:en",
"arxiv:2011.03327",
"region:us"
] | nanyy1025 | null | null | null | 2 | 186 | ---
task_categories:
- text-classification
- zero-shot-classification
language:
- en
---
Constraint@AAAI2021 - COVID19 Fake News Detection in English
```
@misc{patwa2020fighting,
title={Fighting an Infodemic: COVID-19 Fake News Dataset},
author={Parth Patwa and Shivam Sharma and Srinivas PYKL and Vineeth Guptha and Gitanjali Kumari and Md Shad Akhtar and Asif Ekbal and Amitava Das and Tanmoy Chakraborty},
year={2020},
eprint={2011.03327},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
Chinese-Vicuna/guanaco_belle_merge_v1.0 | 2023-03-30T07:49:30.000Z | [
"language:zh",
"language:en",
"language:ja",
"license:gpl-3.0",
"region:us"
] | Chinese-Vicuna | null | null | null | 79 | 186 | ---
license: gpl-3.0
language:
- zh
- en
- ja
---
Thanks for [Guanaco Dataset](https://huggingface.co/datasets/JosephusCheung/GuanacoDataset) and [Belle Dataset](https://huggingface.co/datasets/BelleGroup/generated_train_0.5M_CN)
This dataset was created by merging the above two datasets in a certain format so that they can be used for training our code [Chinese-Vicuna](https://github.com/Facico/Chinese-Vicuna) |
pbaoo2705/processed_dataset_v2 | 2023-09-06T05:27:28.000Z | [
"region:us"
] | pbaoo2705 | null | null | null | 0 | 186 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: pubid
dtype: int32
- name: question
dtype: string
- name: context
dtype: string
- name: long_answer
dtype: string
- name: final_decision
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 9013737
num_examples: 5000
- name: test
num_bytes: 1797886
num_examples: 1000
download_size: 6294228
dataset_size: 10811623
---
# Dataset Card for "processed_dataset_v2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_automl_10000_eye_movements_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T03:32:07.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 186 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 155715478
dataset_size: 472880000
---
# Dataset Card for "autotree_automl_10000_eye_movements_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_automl_10000_Higgs_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T06:25:57.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 186 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 288073393
dataset_size: 472880000
---
# Dataset Card for "autotree_automl_10000_Higgs_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
HuggingFaceH4/surge_instruct_llama2 | 2023-09-17T02:54:46.000Z | [
"region:us"
] | HuggingFaceH4 | null | null | null | 0 | 186 | ---
dataset_info:
features:
- name: prompt
dtype: string
- name: prompt_id
dtype: string
- name: messages
list:
- name: content
dtype: string
- name: role
dtype: string
- name: meta
struct:
- name: category
dtype: string
- name: source
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 28526320
num_examples: 9500
- name: test
num_bytes: 1530441
num_examples: 500
download_size: 18846908
dataset_size: 30056761
---
# Dataset Card for "surge_instruct_llama2"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
sem_eval_2014_task_1 | 2023-01-25T14:43:53.000Z | [
"task_categories:text-classification",
"task_ids:text-scoring",
"task_ids:natural-language-inference",
"task_ids:semantic-similarity-scoring",
"annotations_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:extended|other-ImageFlickr and SemEval-2012 STS MSR-Video Descriptions",
"language:en",
"license:cc-by-4.0",
"region:us"
] | null | The SemEval-2014 Task 1 focuses on Evaluation of Compositional Distributional Semantic Models
on Full Sentences through Semantic Relatedness and Entailment. The task was designed to
predict the degree of relatedness between two sentences and to detect the entailment
relation holding between them. | @inproceedings{inproceedings,
author = {Marelli, Marco and Bentivogli, Luisa and Baroni, Marco and Bernardi, Raffaella and Menini, Stefano and Zamparelli, Roberto},
year = {2014},
month = {08},
pages = {},
title = {SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment},
doi = {10.3115/v1/S14-2001}
} | null | 1 | 185 | ---
annotations_creators:
- crowdsourced
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- extended|other-ImageFlickr and SemEval-2012 STS MSR-Video Descriptions
task_categories:
- text-classification
task_ids:
- text-scoring
- natural-language-inference
- semantic-similarity-scoring
pretty_name: SemEval 2014 - Task 1
dataset_info:
features:
- name: sentence_pair_id
dtype: int64
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: relatedness_score
dtype: float32
- name: entailment_judgment
dtype:
class_label:
names:
'0': NEUTRAL
'1': ENTAILMENT
'2': CONTRADICTION
splits:
- name: train
num_bytes: 540296
num_examples: 4500
- name: test
num_bytes: 592320
num_examples: 4927
- name: validation
num_bytes: 60981
num_examples: 500
download_size: 197230
dataset_size: 1193597
---
# Dataset Card for SemEval 2014 - Task 1
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [SemEval-2014 Task 1](https://alt.qcri.org/semeval2014/task1/)
- **Repository:**
- **Paper:** [Aclweb](https://www.aclweb.org/anthology/S14-2001/)
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@ashmeet13](https://github.com/ashmeet13) for adding this dataset. |
mozilla-foundation/common_voice_2_0 | 2023-07-29T15:59:58.000Z | [
"task_categories:automatic-speech-recognition",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:multilingual",
"source_datasets:extended|common_voice",
"license:cc0-1.0",
"arxiv:1912.06670",
"region:us"
] | mozilla-foundation | null | @inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
} | null | 0 | 185 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
license:
- cc0-1.0
multilinguality:
- multilingual
size_categories:
br:
- 10K<n<100K
ca:
- 10K<n<100K
cnh:
- 1K<n<10K
cv:
- 1K<n<10K
cy:
- 10K<n<100K
de:
- 100K<n<1M
dv:
- 1K<n<10K
en:
- 100K<n<1M
eo:
- 10K<n<100K
es:
- 10K<n<100K
et:
- 1K<n<10K
eu:
- 10K<n<100K
fr:
- 100K<n<1M
ga-IE:
- 1K<n<10K
it:
- 10K<n<100K
kab:
- 100K<n<1M
ky:
- 10K<n<100K
mn:
- 1K<n<10K
nl:
- 10K<n<100K
ru:
- 10K<n<100K
rw:
- 1K<n<10K
sah:
- 1K<n<10K
sl:
- 1K<n<10K
sv-SE:
- 1K<n<10K
tr:
- 1K<n<10K
tt:
- 10K<n<100K
zh-CN:
- 1K<n<10K
zh-TW:
- 10K<n<100K
source_datasets:
- extended|common_voice
paperswithcode_id: common-voice
pretty_name: Common Voice Corpus 2
language_bcp47:
- br
- ca
- cnh
- cv
- cy
- de
- dv
- en
- eo
- es
- et
- eu
- fr
- ga-IE
- it
- kab
- ky
- mn
- nl
- ru
- rw
- sah
- sl
- sv-SE
- tr
- tt
- zh-CN
- zh-TW
extra_gated_prompt: By clicking on “Access repository” below, you also agree to not
attempt to determine the identity of speakers in the Common Voice dataset.
task_categories:
- automatic-speech-recognition
---
# Dataset Card for Common Voice Corpus 2
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://commonvoice.mozilla.org/en/datasets
- **Repository:** https://github.com/common-voice/common-voice
- **Paper:** https://arxiv.org/abs/1912.06670
- **Leaderboard:** https://paperswithcode.com/dataset/common-voice
- **Point of Contact:** [Anton Lozhkov](mailto:anton@huggingface.co)
### Dataset Summary
The Common Voice dataset consists of a unique MP3 and corresponding text file.
Many of the 2366 recorded hours in the dataset also include demographic metadata like age, sex, and accent
that can help improve the accuracy of speech recognition engines.
The dataset currently consists of 1872 validated hours in 28 languages, but more voices and languages are always added.
Take a look at the [Languages](https://commonvoice.mozilla.org/en/languages) page to request a language or start contributing.
### Supported Tasks and Leaderboards
The results for models trained on the Common Voice datasets are available via the
[🤗 Speech Bench](https://huggingface.co/spaces/huggingface/hf-speech-bench)
### Languages
```
Basque, Breton, Catalan, Chinese (China), Chinese (Taiwan), Chuvash, Dhivehi, Dutch, English, Esperanto, Estonian, French, German, Hakha Chin, Irish, Italian, Kabyle, Kinyarwanda, Kyrgyz, Mongolian, Russian, Sakha, Slovenian, Spanish, Swedish, Tatar, Turkish, Welsh
```
## Dataset Structure
### Data Instances
A typical data point comprises the `path` to the audio file and its `sentence`.
Additional fields include `accent`, `age`, `client_id`, `up_votes`, `down_votes`, `gender`, `locale` and `segment`.
```python
{
'client_id': 'd59478fbc1ee646a28a3c652a119379939123784d99131b865a89f8b21c81f69276c48bd574b81267d9d1a77b83b43e6d475a6cfc79c232ddbca946ae9c7afc5',
'path': 'et/clips/common_voice_et_18318995.mp3',
'audio': {
'path': 'et/clips/common_voice_et_18318995.mp3',
'array': array([-0.00048828, -0.00018311, -0.00137329, ..., 0.00079346, 0.00091553, 0.00085449], dtype=float32),
'sampling_rate': 48000
},
'sentence': 'Tasub kokku saada inimestega, keda tunned juba ammust ajast saati.',
'up_votes': 2,
'down_votes': 0,
'age': 'twenties',
'gender': 'male',
'accent': '',
'locale': 'et',
'segment': ''
}
```
### Data Fields
`client_id` (`string`): An id for which client (voice) made the recording
`path` (`string`): The path to the audio file
`audio` (`dict`): A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the `"audio"` column, *i.e.* `dataset[0]["audio"]` should **always** be preferred over `dataset["audio"][0]`.
`sentence` (`string`): The sentence the user was prompted to speak
`up_votes` (`int64`): How many upvotes the audio file has received from reviewers
`down_votes` (`int64`): How many downvotes the audio file has received from reviewers
`age` (`string`): The age of the speaker (e.g. `teens`, `twenties`, `fifties`)
`gender` (`string`): The gender of the speaker
`accent` (`string`): Accent of the speaker
`locale` (`string`): The locale of the speaker
`segment` (`string`): Usually an empty field
### Data Splits
The speech material has been subdivided into portions for dev, train, test, validated, invalidated, reported and other.
The validated data is data that has been validated with reviewers and received upvotes that the data is of high quality.
The invalidated data is data has been invalidated by reviewers
and received downvotes indicating that the data is of low quality.
The reported data is data that has been reported, for different reasons.
The other data is data that has not yet been reviewed.
The dev, test, train are all data that has been reviewed, deemed of high quality and split into dev, test and train.
## Data Preprocessing Recommended by Hugging Face
The following are data preprocessing steps advised by the Hugging Face team. They are accompanied by an example code snippet that shows how to put them to practice.
Many examples in this dataset have trailing quotations marks, e.g _“the cat sat on the mat.“_. These trailing quotation marks do not change the actual meaning of the sentence, and it is near impossible to infer whether a sentence is a quotation or not a quotation from audio data alone. In these cases, it is advised to strip the quotation marks, leaving: _the cat sat on the mat_.
In addition, the majority of training sentences end in punctuation ( . or ? or ! ), whereas just a small proportion do not. In the dev set, **almost all** sentences end in punctuation. Thus, it is recommended to append a full-stop ( . ) to the end of the small number of training examples that do not end in punctuation.
```python
from datasets import load_dataset
ds = load_dataset("mozilla-foundation/common_voice_2_0", "en", use_auth_token=True)
def prepare_dataset(batch):
"""Function to preprocess the dataset with the .map method"""
transcription = batch["sentence"]
if transcription.startswith('"') and transcription.endswith('"'):
# we can remove trailing quotation marks as they do not affect the transcription
transcription = transcription[1:-1]
if transcription[-1] not in [".", "?", "!"]:
# append a full-stop to sentences that do not end in punctuation
transcription = transcription + "."
batch["sentence"] = transcription
return batch
ds = ds.map(prepare_dataset, desc="preprocess dataset")
```
## Dataset Creation
### Curation Rationale
[Needs More Information]
### Source Data
#### Initial Data Collection and Normalization
[Needs More Information]
#### Who are the source language producers?
[Needs More Information]
### Annotations
#### Annotation process
[Needs More Information]
#### Who are the annotators?
[Needs More Information]
### Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in the Common Voice dataset.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
Public Domain, [CC-0](https://creativecommons.org/share-your-work/public-domain/cc0/)
### Citation Information
```
@inproceedings{commonvoice:2020,
author = {Ardila, R. and Branson, M. and Davis, K. and Henretty, M. and Kohler, M. and Meyer, J. and Morais, R. and Saunders, L. and Tyers, F. M. and Weber, G.},
title = {Common Voice: A Massively-Multilingual Speech Corpus},
booktitle = {Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)},
pages = {4211--4215},
year = 2020
}
```
|
rubend18/ChatGPT-Jailbreak-Prompts | 2023-08-24T18:24:29.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"task_categories:fill-mask",
"task_categories:zero-shot-classification",
"task_categories:table-question-answering",
"size_categories:n<1K",
"language:en",
"language:aa",
"ChatGPT",
"JailbreakPrompts",
"LanguageModeling",
"ArtificialIntelligence",
"TextGeneration",
"Dataset",
"OpenAI",
"Jailbreak",
"Prompts",
"region:us"
] | rubend18 | null | null | null | 26 | 185 | ---
task_categories:
- question-answering
- text-generation
- fill-mask
- zero-shot-classification
- table-question-answering
language:
- en
- aa
tags:
- ChatGPT
- JailbreakPrompts
- LanguageModeling
- ArtificialIntelligence
- TextGeneration
- Dataset
- OpenAI
- Jailbreak
- Prompts
size_categories:
- n<1K
pretty_name: ChatGPT Jailbreak Prompts
---
# Dataset Card for Dataset Name
## Name
ChatGPT Jailbreak Prompts
## Dataset Description
- **Autor:** Rubén Darío Jaramillo
- **Email:** rubend18@hotmail.com
- **WhatsApp:** +593 93 979 6676
### Dataset Summary
ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT.
### Languages
[English] |
allocine | 2023-01-25T14:26:09.000Z | [
"task_categories:text-classification",
"task_ids:sentiment-classification",
"annotations_creators:no-annotation",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:fr",
"license:mit",
"region:us"
] | null | Allocine Dataset: A Large-Scale French Movie Reviews Dataset.
This is a dataset for binary sentiment classification, made of user reviews scraped from Allocine.fr.
It contains 100k positive and 100k negative reviews divided into 3 balanced splits: train (160k reviews), val (20k) and test (20k). | @misc{blard2019allocine,
author = {Blard, Theophile},
title = {french-sentiment-analysis-with-bert},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished={\\url{https://github.com/TheophileBlard/french-sentiment-analysis-with-bert}},
} | null | 6 | 184 | ---
annotations_creators:
- no-annotation
language_creators:
- found
language:
- fr
license:
- mit
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- sentiment-classification
paperswithcode_id: allocine
pretty_name: Allociné
dataset_info:
features:
- name: review
dtype: string
- name: label
dtype:
class_label:
names:
'0': neg
'1': pos
config_name: allocine
splits:
- name: train
num_bytes: 91330696
num_examples: 160000
- name: validation
num_bytes: 11546250
num_examples: 20000
- name: test
num_bytes: 11547697
num_examples: 20000
download_size: 66625305
dataset_size: 114424643
train-eval-index:
- config: allocine
task: text-classification
task_id: multi_class_classification
splits:
train_split: train
eval_split: test
col_mapping:
review: text
label: target
metrics:
- type: accuracy
name: Accuracy
- type: f1
name: F1 macro
args:
average: macro
- type: f1
name: F1 micro
args:
average: micro
- type: f1
name: F1 weighted
args:
average: weighted
- type: precision
name: Precision macro
args:
average: macro
- type: precision
name: Precision micro
args:
average: micro
- type: precision
name: Precision weighted
args:
average: weighted
- type: recall
name: Recall macro
args:
average: macro
- type: recall
name: Recall micro
args:
average: micro
- type: recall
name: Recall weighted
args:
average: weighted
---
# Dataset Card for Allociné
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:**
- **Repository:** [Allociné dataset repository](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/tree/master/allocine_dataset)
- **Paper:**
- **Leaderboard:**
- **Point of Contact:** [Théophile Blard](mailto:theophile.blard@gmail.com)
### Dataset Summary
The Allociné dataset is a French-language dataset for sentiment analysis. The texts are movie reviews written between 2006 and 2020 by members of the [Allociné.fr](https://www.allocine.fr/) community for various films. It contains 100k positive and 100k negative reviews divided into train (160k), validation (20k), and test (20k).
### Supported Tasks and Leaderboards
- `text-classification`, `sentiment-classification`: The dataset can be used to train a model for sentiment classification. The model performance is evaluated based on the accuracy of the predicted labels as compared to the given labels in the dataset. A BERT-based model, [tf-allociné](https://huggingface.co/tblard/tf-allocine), achieves 97.44% accuracy on the test set.
### Languages
The text is in French, as spoken by users of the [Allociné.fr](https://www.allocine.fr/) website. The BCP-47 code for French is fr.
## Dataset Structure
### Data Instances
Each data instance contains the following features: _review_ and _label_. In the Hugging Face distribution of the dataset, the _label_ has 2 possible values, _0_ and _1_, which correspond to _negative_ and _positive_ respectively. See the [Allociné corpus viewer](https://huggingface.co/datasets/viewer/?dataset=allocine) to explore more examples.
An example from the Allociné train set looks like the following:
```
{'review': 'Premier film de la saga Kozure Okami, "Le Sabre de la vengeance" est un très bon film qui mêle drame et action, et qui, en 40 ans, n'a pas pris une ride.',
'label': 1}
```
### Data Fields
- 'review': a string containing the review text
- 'label': an integer, either _0_ or _1_, indicating a _negative_ or _positive_ review, respectively
### Data Splits
The Allociné dataset has 3 splits: _train_, _validation_, and _test_. The splits contain disjoint sets of movies. The following table contains the number of reviews in each split and the percentage of positive and negative reviews.
| Dataset Split | Number of Instances in Split | Percent Negative Reviews | Percent Positive Reviews |
| ------------- | ---------------------------- | ------------------------ | ------------------------ |
| Train | 160,000 | 49.6% | 50.4% |
| Validation | 20,000 | 51.0% | 49.0% |
| Test | 20,000 | 52.0% | 48.0% |
## Dataset Creation
### Curation Rationale
The Allociné dataset was developed to support large-scale sentiment analysis in French. It was released alongside the [tf-allociné](https://huggingface.co/tblard/tf-allocine) model and used to compare the performance of several language models on this task.
### Source Data
#### Initial Data Collection and Normalization
The reviews and ratings were collected using a list of [film page urls](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_films_urls.txt) and the [allocine_scraper.py](https://github.com/TheophileBlard/french-sentiment-analysis-with-bert/blob/master/allocine_dataset/allocine_scraper.py) tool. Up to 30 reviews were collected for each film.
The reviews were originally labeled with a rating from 0.5 to 5.0 with a step of 0.5 between each rating. Ratings less than or equal to 2 are labeled as negative and ratings greater than or equal to 4 are labeled as positive. Only reviews with less than 2000 characters are included in the dataset.
#### Who are the source language producers?
The dataset contains movie reviews produced by the online community of the [Allociné.fr](https://www.allocine.fr/) website.
### Annotations
The dataset does not contain any additional annotations.
#### Annotation process
[N/A]
#### Who are the annotators?
[N/A]
### Personal and Sensitive Information
Reviewer usernames or personal information were not collected with the reviews, but could potentially be recovered. The content of each review may include information and opinions about the film's actors, film crew, and plot.
## Considerations for Using the Data
### Social Impact of Dataset
Sentiment classification is a complex task which requires sophisticated language understanding skills. Successful models can support decision-making based on the outcome of the sentiment analysis, though such models currently require a high degree of domain specificity.
It should be noted that the community represented in the dataset may not represent any downstream application's potential users, and the observed behavior of a model trained on this dataset may vary based on the domain and use case.
### Discussion of Biases
The Allociné website lists a number of topics which violate their [terms of service](https://www.allocine.fr/service/conditions.html#charte). Further analysis is needed to determine the extent to which moderators have successfully removed such content.
### Other Known Limitations
The limitations of the Allociné dataset have not yet been investigated, however [Staliūnaitė and Bonfil (2017)](https://www.aclweb.org/anthology/W17-5410.pdf) detail linguistic phenomena that are generally present in sentiment analysis but difficult for models to accurately label, such as negation, adverbial modifiers, and reviewer pragmatics.
## Additional Information
### Dataset Curators
The Allociné dataset was collected by Théophile Blard.
### Licensing Information
The Allociné dataset is licensed under the [MIT License](https://opensource.org/licenses/MIT).
### Citation Information
> Théophile Blard, French sentiment analysis with BERT, (2020), GitHub repository, <https://github.com/TheophileBlard/french-sentiment-analysis-with-bert>
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@TheophileBlard](https://github.com/TheophileBlard), [@lewtun](https://github.com/lewtun) and [@mcmillanmajora](https://github.com/mcmillanmajora) for adding this dataset. |
squad_it | 2023-04-05T13:40:37.000Z | [
"task_categories:question-answering",
"task_ids:open-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:machine-generated",
"language_creators:machine-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:extended|squad",
"language:it",
"license:unknown",
"region:us"
] | null | SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is
split into training and test sets to support the replicability of the benchmarking of QA systems: | @InProceedings{10.1007/978-3-030-03840-3_29,
author={Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto},
editor={Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
title={Neural Learning for Question Answering in Italian},
booktitle={AI*IA 2018 -- Advances in Artificial Intelligence},
year={2018},
publisher={Springer International Publishing},
address={Cham},
pages={389--402},
isbn={978-3-030-03840-3}
} | null | 2 | 184 | ---
annotations_creators:
- machine-generated
language_creators:
- machine-generated
language:
- it
language_bcp47:
- it-IT
license:
- unknown
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- extended|squad
task_categories:
- question-answering
task_ids:
- open-domain-qa
- extractive-qa
paperswithcode_id: squad-it
pretty_name: SQuAD-it
dataset_info:
features:
- name: id
dtype: string
- name: context
dtype: string
- name: question
dtype: string
- name: answers
sequence:
- name: text
dtype: string
- name: answer_start
dtype: int32
splits:
- name: train
num_bytes: 50864824
num_examples: 54159
- name: test
num_bytes: 7858336
num_examples: 7609
download_size: 8776531
dataset_size: 58723160
---
# Dataset Card for "squad_it"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/crux82/squad-it](https://github.com/crux82/squad-it)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 8.78 MB
- **Size of the generated dataset:** 58.79 MB
- **Total amount of disk used:** 67.57 MB
### Dataset Summary
SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset
into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian.
The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The dataset is
split into training and test sets to support the replicability of the benchmarking of QA systems:
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 8.78 MB
- **Size of the generated dataset:** 58.79 MB
- **Total amount of disk used:** 67.57 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"answers": "{\"answer_start\": [243, 243, 243, 243, 243], \"text\": [\"evitare di essere presi di mira dal boicottaggio\", \"evitare di essere pres...",
"context": "\"La crisi ha avuto un forte impatto sulle relazioni internazionali e ha creato una frattura all' interno della NATO. Alcune nazi...",
"id": "5725b5a689a1e219009abd28",
"question": "Perchè le nazioni europee e il Giappone si sono separati dagli Stati Uniti durante la crisi?"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `id`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
### Data Splits
| name | train | test |
| ------- | ----: | ---: |
| default | 54159 | 7609 |
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@InProceedings{10.1007/978-3-030-03840-3_29,
author="Croce, Danilo and Zelenanska, Alexandra and Basili, Roberto",
editor="Ghidini, Chiara and Magnini, Bernardo and Passerini, Andrea and Traverso, Paolo",
title="Neural Learning for Question Answering in Italian",
booktitle="AI*IA 2018 -- Advances in Artificial Intelligence",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="389--402",
isbn="978-3-030-03840-3"
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@albertvillanova](https://github.com/albertvillanova), [@mariamabarham](https://github.com/mariamabarham), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
keshan/wit-dataset | 2021-08-07T18:15:42.000Z | [
"region:us"
] | keshan | \\nWikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset.
WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages.
Its size enables WIT to be used as a pretraining dataset for multimodal machine learning models. | @article{srinivasan2021wit,
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
journal={arXiv preprint arXiv:2103.01913},
year={2021}
} | null | 1 | 184 | https://github.com/google-research-datasets/wit
Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset.
WIT is composed of a curated set of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages.
```
@article{srinivasan2021wit,
title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
journal={arXiv preprint arXiv:2103.01913},
year={2021}
}
``` |
zzliang/GRIT | 2023-07-04T06:40:28.000Z | [
"task_categories:text-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:zero-shot-classification",
"task_ids:image-captioning",
"task_ids:visual-question-answering",
"multilinguality:monolingual",
"size_categories:100M<n<1B",
"source_datasets:COYO-700M",
"language:en",
"license:ms-pl",
"image-text-bounding-box pairs",
"image-text pairs",
"arxiv:2306.14824",
"region:us"
] | zzliang | null | null | null | 37 | 184 | ---
license: ms-pl
language:
- en
multilinguality:
- monolingual
pretty_name: GRIT
size_categories:
- 100M<n<1B
source_datasets:
- COYO-700M
tags:
- image-text-bounding-box pairs
- image-text pairs
task_categories:
- text-to-image
- image-to-text
- object-detection
- zero-shot-classification
task_ids:
- image-captioning
- visual-question-answering
---
# GRIT: Large-Scale Training Corpus of Grounded Image-Text Pairs
### Dataset Description
- **Repository:** [Microsoft unilm](https://github.com/microsoft/unilm/tree/master/kosmos-2)
- **Paper:** [Kosmos-2](https://arxiv.org/abs/2306.14824)
### Dataset Summary
We introduce GRIT, a large-scale dataset of Grounded Image-Text pairs, which is created based on image-text pairs from [COYO-700M](https://github.com/kakaobrain/coyo-dataset) and LAION-2B. We construct a pipeline to extract and link text spans (i.e., noun phrases, and referring expressions) in the caption to their corresponding image regions. More details can be found in the [paper](https://arxiv.org/abs/2306.14824).
### Supported Tasks
During the construction, we excluded the image-caption pairs if no bounding boxes are retained. This procedure resulted in a high-quality image-caption subset of COYO-700M, which we will validate in the future.
Furthermore, this dataset contains text-span-bounding-box pairs. Thus, it can be used in many location-aware mono/multimodal tasks, such as phrase grounding, referring expression comprehension, referring expression generation, and open-world object detection.
### Data Instance
One instance is
```python
{
'key': '000373938',
'clip_similarity_vitb32': 0.353271484375,
'clip_similarity_vitl14': 0.2958984375,
'id': 1795296605919,
'url': "https://www.thestrapsaver.com/wp-content/uploads/customerservice-1.jpg",
'caption': 'a wire hanger with a paper cover that reads we heart our customers',
'width': 1024,
'height': 693,
'noun_chunks': [[19, 32, 0.019644069503434333, 0.31054004033406574, 0.9622142865754519, 0.9603442351023356, 0.79298526], [0, 13, 0.019422357885505368, 0.027634161214033764, 0.9593302408854166, 0.969467560450236, 0.67520964]],
'ref_exps': [[19, 66, 0.019644069503434333, 0.31054004033406574, 0.9622142865754519, 0.9603442351023356, 0.79298526], [0, 66, 0.019422357885505368, 0.027634161214033764, 0.9593302408854166, 0.969467560450236, 0.67520964]]
}
```
- `key`: The generated file name when using img2dataset to download COYO-700M (omit it).
- `clip_similarity_vitb32`: The cosine similarity between text and image(ViT-B/32) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP), provided by COYO-700M.
- `clip_similarity_vitl14`: The cosine similarity between text and image(ViT-L/14) embeddings by [OpenAI CLIP](https://github.com/openai/CLIP), provided by COYO-700M.
- `id`: Unique 64-bit integer ID in COYO-700M.
- `url`: The image URL.
- `caption`: The corresponding caption.
- `width`: The width of the image.
- `height`: The height of the image.
- `noun_chunks`: The noun chunks (extracted by [spaCy](https://spacy.io/)) that have associated bounding boxes (predicted by [GLIP](https://github.com/microsoft/GLIP)). The items in the children list respectively represent 'Start of the noun chunk in caption', 'End of the noun chunk in caption', 'normalized x_min', 'normalized y_min', 'normalized x_max', 'normalized y_max', 'confidence score'.
- `ref_exps`: The corresponding referring expressions. If a noun chunk has no expansion, we just copy it.
### Download image
We recommend to use [img2dataset](https://github.com/rom1504/img2dataset) tool to download the images.
1. Download the metadata. You can download it by cloning current repository:
```bash
git lfs install
git clone https://huggingface.co/datasets/zzliang/GRIT
```
2. Install [img2dataset](https://github.com/rom1504/img2dataset).
```bash
pip install img2dataset
```
3. Download images
You need to replace `/path/to/GRIT_dataset/grit-20m` with the local path to this repository.
```bash
img2dataset --url_list /path/to/GRIT_dataset/grit-20m --input_format "parquet"\
--url_col "url" --caption_col "caption" --output_format webdataset \
--output_folder /tmp/grit --processes_count 4 --thread_count 64 --image_size 256 \
--resize_only_if_bigger=True --resize_mode="keep_ratio" --skip_reencode=True \
--save_additional_columns '["id","noun_chunks","ref_exps","clip_similarity_vitb32","clip_similarity_vitl14"]' \
--enable_wandb False
```
You can adjust some parameters according to your actual needs (e.g., `processes_count`, `thread_count`, `image_size`, `save_additional_columns`).
More img2dataset hyper-parameters can be found in [here](https://github.com/rom1504/img2dataset#api).
### Citation Information
If you apply this dataset to any project and research, please cite our paper and coyo-700m:
```
@article{Kosmos2,
title={Kosmos-2: Grounding Multimodal Large Language Models to the World},
author={Zhiliang Peng and Wenhui Wang and Li Dong and Yaru Hao and Shaohan Huang and Shuming Ma and Furu Wei},
journal={ArXiv},
year={2023},
volume={abs/2306.14824}
}
@misc{kakaobrain2022coyo-700m,
title = {COYO-700M: Image-Text Pair Dataset},
author = {Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, Saehoon Kim},
year = {2022},
howpublished = {\url{https://github.com/kakaobrain/coyo-dataset}},
}
``` |
nampdn-ai/tiny-orca-textbooks | 2023-09-28T02:15:06.000Z | [
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-sa-4.0",
"arxiv:2309.05463",
"arxiv:2305.07759",
"region:us"
] | nampdn-ai | null | null | null | 5 | 184 | ---
task_categories:
- text-generation
language:
- en
pretty_name: Tiny Orca Textbooks
size_categories:
- 100K<n<1M
license: cc-by-nc-sa-4.0
---
# Textbook-like Dataset: A Comprehensive Resource for Text-Based Skills Development in Small Language Models
This dataset is a collection of **147k synthetic textbooks** designed to enhance the text-based skills of small language models. The curriculum is meticulously structured to progress from simple to complex tasks, ensuring a gradual and effective learning experience during pretraining or finetuning SLMs.
The inspiration for this dataset comes from the technical report paper, [Textbooks Are All You Need II: phi-1.5 technical report](https://arxiv.org/abs/2309.05463). The source texts incorporated in this dataset are derived from the [OpenOrca](https://huggingface.co/datasets/Open-Orca/OpenOrca) dataset, a well-known resource in the field.
Emphasizing text-based skills, this dataset serves as a practical reasoning for small language models to learn and do exercise, providing them with a diverse range of skills to learn and adapt from. The step-by-step progression mirrors the structure of a textbook, making it an ideal in-context learning sample.
### Disclaimer
While every effort has been made to ensure the accuracy of the information contained within this dataset, please note that it is provided 'as is' and without any warranties.
The use of the `textbook` field in this dataset is intended for research purposes only. You are advised to verify any information obtained from this dataset before acting upon it.
## Tiny Series
Explore the possibilities and limitations of building Small Language Models with these tiny gems of data!
- [TinyStories](https://arxiv.org/abs/2305.07759): The paper that sparked my interest in the journey of the tiny-* series.
- [tiny-codes](https://huggingface.co/datasets/nampdn-ai/tiny-codes): Collection of 1.6M short and clear code snippets that can help LLM models learn how to reason.
- [tiny-textbooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks): 420k "things of internet" synthetic textbooks.
- [tiny-webtext](https://huggingface.co/datasets/nampdn-ai/tiny-webtext): A 6GB (4.5M records) variety of diverse webtext enriched with critical thinking methods to make unbiased English dataset.
- [tiny-lessons](https://huggingface.co/datasets/nampdn-ai/tiny-lessons): Subset of *tiny-textbooks* dataset, various lessons about "things of internet" augmented in a bite-sized textbook Markdown format.
- [tiny-bridgedict](https://huggingface.co/datasets/nampdn-ai/tiny-bridgedict): A dataset that links and transfers knowledge between English, Vietnamese, Chinese in a tiny multilingual models.
### Others small HQ datasets with textbook-like quality
- [devdocs.io](https://huggingface.co/datasets/nampdn-ai/devdocs.io): FreeCodeCamp has provided 189k comprehensive API documentation across a wide range of tech stacks and programming languages.
- [sciphi-python-textbook](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-python-textbook)
- [textbook_quality_programming](https://huggingface.co/datasets/vikp/textbook_quality_programming)
- [sciphi-textbooks-are-all-you-need](https://huggingface.co/datasets/emrgnt-cmplxty/sciphi-textbooks-are-all-you-need)
|
yzhuang/autotree_pmlb_10000_magic_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T05:44:01.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 184 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 188904676
dataset_size: 472880000
---
# Dataset Card for "autotree_pmlb_10000_magic_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
tyzhu/squad_v2_1000_0.50_id | 2023-09-12T16:42:18.000Z | [
"region:us"
] | tyzhu | null | null | null | 0 | 184 | ---
dataset_info:
features:
- name: inputs
dtype: string
- name: targets
dtype: string
- name: question
dtype: string
- name: context
dtype: string
- name: answers
struct:
- name: answer_start
sequence: int64
- name: text
sequence: string
- name: id
dtype: string
splits:
- name: train
num_bytes: 97308726.73032843
num_examples: 55568
- name: validation
num_bytes: 1917601
num_examples: 1000
download_size: 4274826
dataset_size: 99226327.73032843
---
# Dataset Card for "squad_v2_1000_0.50_id"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
result-kand2-sdxl-wuerst-karlo/e8491cc1 | 2023-10-03T01:18:56.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 184 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 168
num_examples: 10
download_size: 1314
dataset_size: 168
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "e8491cc1"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
jjonhwa/dolly-ko | 2023-10-08T09:55:19.000Z | [
"region:us"
] | jjonhwa | null | null | null | 0 | 184 | ---
dataset_info:
features:
- name: text
dtype: string
splits:
- name: train
num_bytes: 14238792
num_examples: 15011
download_size: 8006189
dataset_size: 14238792
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "dolly-ko"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
definite_pronoun_resolution | 2023-04-05T10:04:44.000Z | [
"task_categories:token-classification",
"task_ids:word-sense-disambiguation",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Composed by 30 students from one of the author's undergraduate classes. These
sentence pairs cover topics ranging from real events (e.g., Iran's plan to
attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
Batman) and purely imaginary situations, largely reflecting the pop culture as
perceived by the American kids born in the early 90s. Each annotated example
spans four lines: the first line contains the sentence, the second line contains
the target pronoun, the third line contains the two candidate antecedents, and
the fourth line contains the correct antecedent. If the target pronoun appears
more than once in the sentence, its first occurrence is the one to be resolved. | @inproceedings{rahman2012resolving,
title={Resolving complex cases of definite pronouns: the winograd schema challenge},
author={Rahman, Altaf and Ng, Vincent},
booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
pages={777--789},
year={2012},
organization={Association for Computational Linguistics}
} | null | 3 | 183 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- word-sense-disambiguation
paperswithcode_id: definite-pronoun-resolution-dataset
pretty_name: Definite Pronoun Resolution Dataset
dataset_info:
features:
- name: sentence
dtype: string
- name: pronoun
dtype: string
- name: candidates
sequence: string
length: 2
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
config_name: plain_text
splits:
- name: test
num_bytes: 71691
num_examples: 564
- name: train
num_bytes: 171511
num_examples: 1322
download_size: 227452
dataset_size: 243202
---
# Dataset Card for "definite_pronoun_resolution"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://www.hlt.utdallas.edu/~vince/data/emnlp12/](https://www.hlt.utdallas.edu/~vince/data/emnlp12/)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 0.23 MB
- **Size of the generated dataset:** 0.24 MB
- **Total amount of disk used:** 0.47 MB
### Dataset Summary
Composed by 30 students from one of the author's undergraduate classes. These
sentence pairs cover topics ranging from real events (e.g., Iran's plan to
attack the Saudi ambassador to the U.S.) to events/characters in movies (e.g.,
Batman) and purely imaginary situations, largely reflecting the pop culture as
perceived by the American kids born in the early 90s. Each annotated example
spans four lines: the first line contains the sentence, the second line contains
the target pronoun, the third line contains the two candidate antecedents, and
the fourth line contains the correct antecedent. If the target pronoun appears
more than once in the sentence, its first occurrence is the one to be resolved.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### plain_text
- **Size of downloaded dataset files:** 0.23 MB
- **Size of the generated dataset:** 0.24 MB
- **Total amount of disk used:** 0.47 MB
An example of 'train' looks as follows.
```
{
"candidates": ["coreference resolution", "chunking"],
"label": 0,
"pronoun": "it",
"sentence": "There is currently more work on coreference resolution than on chunking because it is a problem that is still far from being solved."
}
```
### Data Fields
The data fields are the same among all splits.
#### plain_text
- `sentence`: a `string` feature.
- `pronoun`: a `string` feature.
- `candidates`: a `list` of `string` features.
- `label`: a classification label, with possible values including `0` (0), `1` (1).
### Data Splits
| name |train|test|
|----------|----:|---:|
|plain_text| 1322| 564|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@inproceedings{rahman2012resolving,
title={Resolving complex cases of definite pronouns: the winograd schema challenge},
author={Rahman, Altaf and Ng, Vincent},
booktitle={Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning},
pages={777--789},
year={2012},
organization={Association for Computational Linguistics}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@patrickvonplaten](https://github.com/patrickvonplaten) for adding this dataset. |
SkelterLabsInc/JaQuAD | 2022-10-25T09:06:40.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ja",
"license:cc-by-sa-3.0",
"arxiv:2202.01764",
"region:us"
] | SkelterLabsInc | null | null | null | 5 | 183 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
- found
language:
- ja
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
paperswithcode_id: null
pretty_name: "JaQuAD: Japanese Question Answering Dataset"
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
---
# Dataset Card for JaQuAD
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splitting](#data-splitting)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Acknowledgements](#acknowledgements)
## Dataset Description
- **Repository:** https://github.com/SkelterLabsInc/JaQuAD
- **Paper:** [JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension]()
- **Point of Contact:** [jaquad@skelterlabs.com](jaquad@skelterlabs.com)
- **Size of dataset files:** 24.6 MB
- **Size of the generated dataset:** 48.6 MB
- **Total amount of disk used:** 73.2 MB
### Dataset Summary
Japanese Question Answering Dataset (JaQuAD), released in 2022, is a
human-annotated dataset created for Japanese Machine Reading Comprehension.
JaQuAD is developed to provide a SQuAD-like QA dataset in Japanese.
JaQuAD contains 39,696 question-answer pairs.
Questions and answers are manually curated by human annotators.
Contexts are collected from Japanese Wikipedia articles.
Fine-tuning [BERT-Japanese](https://huggingface.co/cl-tohoku/bert-base-japanese)
on JaQuAD achieves 78.92% for an F1 score and 63.38% for an exact match.
### Supported Tasks
- `extractive-qa`: This dataset is intended to be used for `extractive-qa`.
### Languages
Japanese (`ja`)
## Dataset Structure
### Data Instances
- **Size of dataset files:** 24.6 MB
- **Size of the generated dataset:** 48.6 MB
- **Total amount of disk used:** 73.2 MB
An example of 'validation':
```python
{
"id": "de-001-00-000",
"title": "イタセンパラ",
"context": "イタセンパラ(板鮮腹、Acheilognathuslongipinnis)は、コイ科のタナゴ亜科タナゴ属に分類される淡水>魚の一種。\n別名はビワタナゴ(琵琶鱮、琵琶鰱)。",
"question": "ビワタナゴの正式名称は何?",
"question_type": "Multiple sentence reasoning",
"answers": {
"text": "イタセンパラ",
"answer_start": 0,
"answer_type": "Object",
},
},
```
### Data Fields
- `id`: a `string` feature.
- `title`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `question_type`: a `string` feature.
- `answers`: a dictionary feature containing:
- `text`: a `string` feature.
- `answer_start`: a `int32` feature.
- `answer_type`: a `string` feature.
### Data Splitting
JaQuAD consists of three sets, `train`, `validation`, and `test`. They were
created from disjoint sets of Wikipedia articles. The `test` set is not publicly
released yet. The following table shows statistics for each set.
Set | Number of Articles | Number of Contexts | Number of Questions
--------------|--------------------|--------------------|--------------------
Train | 691 | 9713 | 31748
Validation | 101 | 1431 | 3939
Test | 109 | 1479 | 4009
## Dataset Creation
### Curation Rationale
The JaQuAD dataset was created by [Skelter Labs](https://skelterlabs.com/) to
provide a SQuAD-like QA dataset in Japanese. Questions are original and based
on Japanese Wikipedia articles.
### Source Data
The articles used for the contexts are from [Japanese Wikipedia](https://ja.wikipedia.org/).
88.7% of articles are from the curated list of Japanese high-quality Wikipedia
articles, e.g., [featured articles](https://ja.wikipedia.org/wiki/Wikipedia:%E8%89%AF%E8%B3%AA%E3%81%AA%E8%A8%98%E4%BA%8B)
and [good articles](https://ja.wikipedia.org/wiki/Wikipedia:%E7%A7%80%E9%80%B8%E3%81%AA%E8%A8%98%E4%BA%8B).
### Annotations
Wikipedia articles were scrapped and divided into one more multiple paragraphs
as contexts. Annotations (questions and answer spans) are written by fluent
Japanese speakers, including natives and non-natives. Annotators were given a
context and asked to generate non-trivial questions about information in the
context.
### Personal and Sensitive Information
No personal or sensitive information is included in this dataset. Dataset
annotators has been manually verified it.
## Considerations for Using the Data
Users should consider that the articles are sampled from Wikipedia articles but
not representative of all Wikipedia articles.
### Social Impact of Dataset
The social biases of this dataset have not yet been investigated.
### Discussion of Biases
The social biases of this dataset have not yet been investigated. Articles and
questions have been selected for quality and diversity.
### Other Known Limitations
The JaQuAD dataset has limitations as follows:
- Most of them are short answers.
- Assume that a question is answerable using the corresponding context.
This dataset is incomplete yet. If you find any errors in JaQuAD, please contact
us.
## Additional Information
### Dataset Curators
Skelter Labs: [https://skelterlabs.com/](https://skelterlabs.com/)
### Licensing Information
The JaQuAD dataset is licensed under the [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/) license.
### Citation Information
```bibtex
@misc{so2022jaquad,
title={{JaQuAD: Japanese Question Answering Dataset for Machine Reading Comprehension}},
author={ByungHoon So and Kyuhong Byun and Kyungwon Kang and Seongjin Cho},
year={2022},
eprint={2202.01764},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Acknowledgements
This work was supported by [TPU Research Cloud (TRC) program](https://sites.research.google/trc/).
For training models, we used cloud TPUs provided by TRC. We also thank
annotators who generated JaQuAD.
|
yxchar/citation_intent-tlm | 2021-11-04T23:47:24.000Z | [
"region:us"
] | yxchar | null | null | null | 2 | 183 | Entry not found |
lighteval/mutual_harness | 2023-08-09T15:50:01.000Z | [
"region:us"
] | lighteval | MuTual is a retrieval-based dataset for multi-turn dialogue reasoning, which is
modified from Chinese high school English listening comprehension test data. | @inproceedings{mutual,
title = "MuTual: A Dataset for Multi-Turn Dialogue Reasoning",
author = "Cui, Leyang and Wu, Yu and Liu, Shujie and Zhang, Yue and Zhou, Ming" ,
booktitle = "Proceedings of the 58th Conference of the Association for Computational Linguistics",
year = "2020",
publisher = "Association for Computational Linguistics",
} | null | 2 | 183 | Entry not found |
yzhuang/autotree_automl_10000_default-of-credit-card-clients_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T04:10:11.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 183 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 122258450
dataset_size: 472880000
---
# Dataset Card for "autotree_automl_10000_default-of-credit-card-clients_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
emrgnt-cmplxty/sciphi-python-textbook | 2023-10-09T15:49:57.000Z | [
"license:llama2",
"arxiv:2306.11644",
"region:us"
] | emrgnt-cmplxty | null | null | null | 30 | 183 | ---
license: llama2
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: formatted_prompt
dtype: string
- name: completion
dtype: string
splits:
- name: train
num_bytes: 1622068845
num_examples: 555072
download_size: 753726617
dataset_size: 1622068845
---
# Reasoning with Language and Code: A SciPhi Collection
## Dataset Description
This dataset, currently **300k fully open source samples, was generated using the [SciPhi repository](https://github.com/emrgnt-cmplxty/SciPhi)**.
The primary objective of this repository is to emphasize the significance of textbook-like high educational value using code snippets. Each code snippet has been meticulously crafted to ensure maximum readability and comprehension. This collection focuses exclusively on Python, in line with the findings from the original paper. Work is ongoing to extend this dataset and introduce new related datasets.
### Source
This dataset was inspired and generated based on the paper "Textbooks Are All You Need" and related open source datasets, e.g. `nampdn-ai/tiny-codes`. These studies demonstrated that LLM models can achieve state-of-the-art results on code-related tasks when trained on high-quality data that mirrors textbooks and exercises. This repository seeks to provide such data for data analysts and ML engineers eager to deepen their understanding of how small LLM models can learn to reason with code.
### Use Cases
Researchers and developers can utilize this dataset to:
- Train and evaluate LLM models on reasoning tasks with code.
- Reproduce the dataset using other LLM models and compare outcomes.
- Forge new prompts from related properties for varied experiments.
### Limitations
Please be aware that this dataset is not designed with production tasks in mind. Further, post-processing has not been extensively carried out and is recommended.
### Questions?
Join us on [Discord here](https://discord.gg/j9GxfbxqAe) or [contact me](mailto:owe@emergentagi.com) directly.
## Dataset Structure
### Features
The dataset includes the following features:
- `prompt_template`: (string) The template used for the prompt.
- `course_type`: (string) Type of course associated with the data.
- `programming_paradigm`: (string) Programming paradigm related to the code.
- `concept`: (string) The main concept or topic of the snippet.
- `depth`: (string) Depth or complexity level of the snippet.
- `additional_context`: (string) Any additional context or information.
- `model`: (string) Model details or type.
- `batch_size`: (int64) Batch size used during generation.
- `temperature`: (int64) Temperature setting during generation.
- `top_p`: (float64) Top-p setting during generation.
- `top_k`: (int64) Top-k setting during generation.
- `max_tokens`: (int64) Maximum tokens setting during generation.
- `generated_text`: (string) The generated text or code snippet.
### Splits
- `train`: 294,144 examples.
## Citation
For the original inspiration and methodology behind this dataset, please cite:
```
@misc{gunasekar2023textbooks,
title={Textbooks Are All You Need},
author={Suriya Gunasekar and Yi Zhang and Jyoti Aneja and Caio César Teodoro Mendes and Allie Del Giorno and Sivakanth Gopi and Mojan Javaheripi and Piero Kauffmann and Gustavo de Rosa and Olli Saarikivi and Adil Salim and Shital Shah and Harkirat Singh Behl and Xin Wang and Sébastien Bubeck and Ronen Eldan and Adam Tauman Kalai and Yin Tat Lee and Yuanzhi Li},
year={2023},
eprint={2306.11644},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
PORTULAN/glue-ptpt | 2023-05-12T12:49:02.000Z | [
"language_creators:machine-generated",
"size_categories:10K<n<100K",
"source_datasets:glue",
"language:pt",
"arxiv:2305.06721",
"region:us"
] | PORTULAN | GLUE-PTPT is an European Portuguese translation of the GLUE benchmark using DeepL Pro. | @misc{Gomes2023,
author = {Luís Gomes and João Rodrigues and João Silva and António Branco and Rodrigo Santos},
title = {GLUE-PTPT -- The General Language Understanding Evaluation benchmark translated to European Portuguese},
year = {2023},
publisher = {Hugging Face},
journal = {Hugging Face dataset},
howpublished = {\\url{https://huggingface.co/datasets/PORTULAN/glue-ptpt}},
} | null | 3 | 182 | ---
language:
- pt
language_creators:
- machine-generated
source_datasets:
- glue
pretty_name: GLUE-PTPT -- The General Language Understanding Evaluation benchmark translated to European Portuguese
size_categories:
- 10K<n<100K
---
# GLUE-PTPT -- The General Language Understanding Evaluation benchmark translated to European Portuguese
This dataset has been created to evaluate [Albertina PT-* models](https://huggingface.co/PORTULAN/albertina-ptpt).
If you use this dataset please cite:
@misc{rodrigues2023advancing,
title={Advancing Neural Encoding of Portuguese with Transformer Albertina PT-*},
author={João Rodrigues and Luís Gomes and João Silva and António Branco and Rodrigo Santos and Henrique Lopes Cardoso and Tomás Osório},
year={2023},
eprint={2305.06721},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Thus far, only 4 tasks have been translated to European Portuguese:
- MRPC
- RTE
- STS-B
- WNLI
The remainder tasks will be added in the future.
See [gluebenchmark.com](https://gluebenchmark.com/) for information about the General Language Understanding Evaluation (GLUE) dataset.
|
yzhuang/autotree_pmlb_10000_Hill_Valley_with_noise_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T04:14:27.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 182 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 172085873
dataset_size: 472880000
---
# Dataset Card for "autotree_pmlb_10000_Hill_Valley_with_noise_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
yzhuang/autotree_pmlb_10000_Hill_Valley_without_noise_sgosdt_l256_dim10_d3_sd0 | 2023-09-07T05:25:19.000Z | [
"region:us"
] | yzhuang | null | null | null | 0 | 182 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: input_x
sequence:
sequence: float32
- name: input_y
sequence:
sequence: float32
- name: input_y_clean
sequence:
sequence: float32
- name: rtg
sequence: float64
- name: status
sequence:
sequence: float32
- name: split_threshold
sequence:
sequence: float32
- name: split_dimension
sequence: int64
splits:
- name: train
num_bytes: 236440000
num_examples: 10000
- name: validation
num_bytes: 236440000
num_examples: 10000
download_size: 179483399
dataset_size: 472880000
---
# Dataset Card for "autotree_pmlb_10000_Hill_Valley_without_noise_sgosdt_l256_dim10_d3_sd0"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
result-kand2-sdxl-wuerst-karlo/78fe0016 | 2023-10-03T01:21:52.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 182 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 173
num_examples: 10
download_size: 1317
dataset_size: 173
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "78fe0016"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
Zaid/quac_expanded | 2021-10-04T19:41:30.000Z | [
"region:us"
] | Zaid | \\nQuestion Answering in Context is a dataset for modeling, understanding,
and participating in information seeking dialog. Data instances consist
of an interactive dialog between two crowd workers: (1) a student who
poses a sequence of freeform questions to learn as much as possible
about a hidden Wikipedia text, and (2) a teacher who answers the questions
by providing short excerpts (spans) from the text. QuAC introduces
challenges not found in existing machine comprehension datasets: its
questions are often more open-ended, unanswerable, or only meaningful
within the dialog context. | \\n@inproceedings{choi-etal-2018-quac,
title = "QUAC: Question answering in context",
abstract = "We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at http://quac.ai.",
author = "Eunsol Choi and He He and Mohit Iyyer and Mark Yatskar and Yih, {Wen Tau} and Yejin Choi and Percy Liang and Luke Zettlemoyer",
year = "2018",
language = "English (US)",
series = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018",
publisher = "Association for Computational Linguistics",
pages = "2174--2184",
editor = "Ellen Riloff and David Chiang and Julia Hockenmaier and Jun'ichi Tsujii",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018",
note = "2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 ; Conference date: 31-10-2018 Through 04-11-2018",
} | null | 0 | 181 | Entry not found |
Bingsu/Human_Action_Recognition | 2022-07-05T02:48:56.000Z | [
"task_categories:image-classification",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:odbl",
"region:us"
] | Bingsu | null | null | null | 7 | 181 | ---
language:
- en
license:
- odbl
pretty_name: Human Action Recognition
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- image-classification
---
## Dataset Description
- **Homepage:** [Human Action Recognition (HAR) Dataset](https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset)
- **Repository:** N/A
- **Paper:** N/A
- **Leaderboard:** N/A
- **Point of Contact:** N/A
## Dataset Summary
A dataset from [kaggle](https://www.kaggle.com/datasets/meetnagadia/human-action-recognition-har-dataset). origin: https://dphi.tech/challenges/data-sprint-76-human-activity-recognition/233/data
### Introduction
- The dataset features 15 different classes of Human Activities.
- The dataset contains about 12k+ labelled images including the validation images.
- Each image has only one human activity category and are saved in separate folders of the labelled classes
### PROBLEM STATEMENT
- Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasing attention in the field of computer vision. Human actions can be represented using various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal, which encode different sources of useful yet distinct information and have various advantages depending on the application scenarios.
- Consequently, lots of existing works have attempted to investigate different types of approaches for HAR using various modalities.
- Your Task is to build an Image Classification Model using CNN that classifies to which class of activity a human is performing.
### About Files
- Train - contains all the images that are to be used for training your model. In this folder you will find 15 folders namely - 'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using_laptop’ which contain the images of the respective human activities.
- Test - contains 5400 images of Human Activities. For these images you are required to make predictions as the respective class names -'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’, ‘sleeping’, texting’, ‘using_laptop’.
- Testing_set.csv - this is the order of the predictions for each image that is to be submitted on the platform. Make sure the predictions you download are with their image’s filename in the same order as given in this file.
- sample_submission: This is a csv file that contains the sample submission for the data sprint.
### Data Fields
The data instances have the following fields:
- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]`.
- `labels`: an `int` classification label. All `test` data is labeled 0.
### Class Label Mappings:
```
{
'calling': 0,
'clapping': 1,
'cycling': 2,
'dancing': 3,
'drinking': 4,
'eating': 5,
'fighting': 6,
'hugging': 7,
'laughing': 8,
'listening_to_music': 9,
'running': 10,
'sitting': 11,
'sleeping': 12,
'texting': 13,
'using_laptop': 14
}
```
### Data Splits
| | train | test |
|---------------|--------|-----:|
| # of examples | 12600 | 5400 |
### Data Size
- download: 311.96 MiB
- generated: 312.59 MiB
- total: 624.55 MiB
```pycon
>>> from datasets import load_dataset
>>> ds = load_dataset("Bingsu/Human_Action_Recognition")
>>> ds
DatasetDict({
test: Dataset({
features: ['image', 'labels'],
num_rows: 5400
})
train: Dataset({
features: ['image', 'labels'],
num_rows: 12600
})
})
>>> ds["train"].features
{'image': Image(decode=True, id=None),
'labels': ClassLabel(num_classes=15, names=['calling', 'clapping', 'cycling', 'dancing', 'drinking', 'eating', 'fighting', 'hugging', 'laughing', 'listening_to_music', 'running', 'sitting', 'sleeping', 'texting', 'using_laptop'], id=None)}
>>> ds["train"][0]
{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=240x160>,
'labels': 11}
``` |
PKU-Alignment/BeaverTails | 2023-07-20T15:33:08.000Z | [
"task_categories:text-classification",
"size_categories:100K<n<1M",
"language:en",
"license:cc-by-nc-4.0",
"safe",
"safety",
"ai-safety",
"moderation",
"rejection-sampling",
"llm",
"lm",
"human-feedback",
"arxiv:2307.04657",
"region:us"
] | PKU-Alignment | null | null | null | 11 | 181 | ---
license: cc-by-nc-4.0
task_categories:
- text-classification
language:
- en
tags:
- safe
- safety
- ai-safety
- moderation
- rejection-sampling
- llm
- lm
- human-feedback
size_categories:
- 100K<n<1M
---
# Dataset Card for BeaverTails
BeaverTails is an AI safety-focused collection comprising a series of datasets.
This repository includes human-labeled data consisting of question-answer (QA) pairs, each identified with their corresponding harm categories.
It should be noted that a single QA pair can be associated with more than one category.
The 14 harm categories are defined as follows:
1. **Animal Abuse:** This involves any form of cruelty or harm inflicted on animals, including physical abuse, neglect, or any behavior that causes distress or harm to an animal. It also includes content that promotes such behavior.
1. **Child Abuse:** This encompasses any form of physical, emotional, or sexual abuse directed toward children. It can also include neglect, exploitation, or any behavior that harms a child or violates their rights. Content that promotes or glorifies such behavior also falls under this category.
1. **Controversial Topics, Politics:** This refers to discussions or statements related to controversial topics, political ideologies, or sensitive issues that could lead to disagreement or conflict. These topics can be divisive and could potentially cause harm if not handled respectfully and thoughtfully.
1. **Discrimination, Stereotype, Injustice:** Discrimination refers to treating people unfairly or unequally based on their personal characteristics or affiliations such as race, age, gender, religion, etc. Stereotyping involves holding simplified and generalized beliefs about particular groups, which often leads to prejudices and discrimination. Injustice pertains to unfair treatment or violation of the rights of individuals or groups.
1. **Drug Abuse, Weapons, Banned Substance:** This category includes the misuse or abuse of illegal drugs, prescription drugs, or any other banned substances. It also involves the unauthorized or illegal possession, use, or trade of weapons.
1. **Financial Crime, Property Crime, Theft:** This includes all forms of illegal activities that lead to financial loss such as fraud, embezzlement, money laundering, bribery, and other forms of corruption. Property crime and theft involve the act of taking someone's property without their consent.
1. **Hate Speech, Offensive Language:** This category encompasses any form of communication or content that expresses intense hostility or violence towards an individual or group based on aspects like race, religion, ethnicity, nationality, gender, sexual orientation, or any other defining characteristics. This also includes language that is considered vulgar, crude, or offensive.
1. **Misinformation Regarding ethics, laws, and safety:** This category involves the spreading of incorrect or misleading information related to ethical issues, laws, regulations, or any matter related to safety. Misinformation can cause harm by influencing people's beliefs or actions negatively.
1. **Non-Violent Unethical Behavior:** This refers to any behavior that is morally or ethically wrong, but does not involve violence. Examples can include dishonest behaviors like lying, cheating, deceiving, spreading rumors, betraying trust, or any other form of unethical conduct.
1. **Privacy Violation:** This refers to actions that invade an individual's privacy, such as unauthorized access, use, disclosure, or collection of personal data. It also includes stalking, doxing, or any other form of intrusion into someone's personal life without their consent.
1. **Self-Harm:** This includes any form of self-inflicted harm, whether physical or emotional, or any content that promotes, glorifies, or encourages self-harm. This can also include discussions of suicidal thoughts or plans.
1. **Sexually Explicit, Adult Content:** This category includes any content that depicts explicit sexual activities or adult themes that are considered inappropriate or offensive. This can include pornography, explicit sexual text or messages, and any other form of sexually explicit material.
1. **Terrorism, Organized Crime:** This pertains to any form of content or action related to terrorism or organized crime, including endorsing or promoting terrorist activities, participating in organized criminal activities, or spreading propaganda for such groups.
1. **Violence, Aiding and Abetting, Incitement:** This involves any form of physical harm, threat, or violent behavior towards individuals or groups. Aiding and abetting refers to the act of helping, supporting, or encouraging such violent behaviors or illegal activities. Incitement pertains to the act of provoking or stirring up harmful, violent, or illegal actions.
**Disclaimer**: The BeaverTails dataset and its family contain content that may be offensive or upsetting.
Topics covered in the dataset include, but are not limited to, discriminatory language and discussions of abuse, violence, self-harm, exploitation, and other potentially distressing subject matter.
Please engage with the dataset responsibly and in accordance with your own personal risk tolerance.
The dataset is intended for research purposes, specifically for research aimed at creating safer and less harmful AI systems.
The views and opinions expressed in the dataset do not represent the views of the PKU-Alignment Team or any of its members.
It is important to emphasize that the dataset should not be used for training dialogue agents, as doing so may likely result in harmful model behavior.
The primary objective of this dataset is to facilitate research that could minimize or prevent the harm caused by AI systems.
## Usage
The code snippet below demonstrates how to load the QA-Classification dataset:
```python
from datasets import load_dataset
# Load the whole dataset
dataset = load_dataset('PKU-Alignment/BeaverTails')
# Load only the round 0 dataset
round0_dataset = load_dataset('PKU-Alignment/BeaverTails', data_dir='round0')
# Load the training dataset
train_dataset = load_dataset('PKU-Alignment/BeaverTails', split='train')
test_dataset = load_dataset('PKU-Alignment/BeaverTails', split='test')
```
## Papers
You can find more information in our Paper:
- **Dataset Paper:** <https://arxiv.org/abs/2307.04657>
## Contact
The original authors host this dataset on GitHub here: https://github.com/PKU-Alignment/beavertails
## License
BeaverTails dataset and its family are released under the CC BY-NC 4.0 License. |
AlignmentLab-AI/agentcode | 2023-10-10T11:53:55.000Z | [
"region:us"
] | AlignmentLab-AI | null | null | null | 2 | 181 | Entry not found |
NamCyan/thevault-docstringstyle | 2023-09-15T18:55:54.000Z | [
"region:us"
] | NamCyan | null | null | null | 0 | 181 | ---
dataset_info:
features:
- name: hexsha
dtype: string
- name: repo
dtype: string
- name: path
dtype: string
- name: license
sequence: string
- name: language
dtype: string
- name: identifier
dtype: string
- name: return_type
dtype: string
- name: original_string
dtype: string
- name: original_docstring
dtype: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: code
dtype: string
- name: code_tokens
sequence: string
- name: short_docstring
dtype: string
- name: short_docstring_tokens
sequence: string
- name: comment
sequence: string
- name: parameters
list:
- name: param
dtype: string
- name: type
dtype: string
- name: docstring_params
struct:
- name: returns
list:
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: type
dtype: string
- name: raises
list:
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: type
dtype: string
- name: params
list:
- name: identifier
dtype: string
- name: type
dtype: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: default
dtype: string
- name: is_optional
dtype: bool
- name: outlier_params
list:
- name: identifier
dtype: string
- name: type
dtype: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: default
dtype: string
- name: is_optional
dtype: bool
- name: others
list:
- name: identifier
dtype: string
- name: docstring
dtype: string
- name: docstring_tokens
sequence: string
- name: instruction
dtype: string
splits:
- name: train
num_bytes: 6545943535
num_examples: 1261519
download_size: 1969238091
dataset_size: 6545943535
---
# Dataset Card for "thevault-docstringstyle"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
SneakyInsect/maestro-rollingsplit | 2023-10-04T13:21:21.000Z | [
"region:us"
] | SneakyInsect | null | null | null | 0 | 181 | ---
dataset_info:
features:
- name: name
dtype: string
- name: start
sequence: float64
- name: duration
sequence: float64
- name: pitch
sequence: float64
- name: velocity
sequence: float64
splits:
- name: train
num_bytes: 745208510
num_examples: 373963
- name: validation
num_bytes: 84002977
num_examples: 42153
- name: test
num_bytes: 97390221
num_examples: 48820
download_size: 144295382
dataset_size: 926601708
---
# Dataset Card for "maestro-rollingsplit"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
mwsc | 2023-04-05T13:33:22.000Z | [
"task_categories:multiple-choice",
"task_ids:multiple-choice-coreference-resolution",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:n<1K",
"source_datasets:extended|winograd_wsc",
"language:en",
"license:cc-by-4.0",
"arxiv:1806.08730",
"region:us"
] | null | Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
This modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing. | @article{McCann2018decaNLP,
title={The Natural Language Decathlon: Multitask Learning as Question Answering},
author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
journal={arXiv preprint arXiv:1806.08730},
year={2018}
} | null | 0 | 180 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: Modified Winograd Schema Challenge (MWSC)
size_categories:
- n<1K
source_datasets:
- extended|winograd_wsc
task_categories:
- multiple-choice
task_ids:
- multiple-choice-coreference-resolution
paperswithcode_id: null
dataset_info:
features:
- name: sentence
dtype: string
- name: question
dtype: string
- name: options
sequence: string
- name: answer
dtype: string
splits:
- name: train
num_bytes: 11022
num_examples: 80
- name: test
num_bytes: 15220
num_examples: 100
- name: validation
num_bytes: 13109
num_examples: 82
download_size: 19197
dataset_size: 39351
---
# Dataset Card for The modified Winograd Schema Challenge (MWSC)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [http://decanlp.com](http://decanlp.com)
- **Repository:** https://github.com/salesforce/decaNLP
- **Paper:** [The Natural Language Decathlon: Multitask Learning as Question Answering](https://arxiv.org/abs/1806.08730)
- **Point of Contact:** [Bryan McCann](mailto:bmccann@salesforce.com), [Nitish Shirish Keskar](mailto:nkeskar@salesforce.com)
- **Size of downloaded dataset files:** 19.20 kB
- **Size of the generated dataset:** 39.35 kB
- **Total amount of disk used:** 58.55 kB
### Dataset Summary
Examples taken from the Winograd Schema Challenge modified to ensure that answers are a single word from the context.
This Modified Winograd Schema Challenge (MWSC) ensures that scores are neither inflated nor deflated by oddities in phrasing.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### default
- **Size of downloaded dataset files:** 0.02 MB
- **Size of the generated dataset:** 0.04 MB
- **Total amount of disk used:** 0.06 MB
An example looks as follows:
```
{
"sentence": "The city councilmen refused the demonstrators a permit because they feared violence.",
"question": "Who feared violence?",
"options": [ "councilmen", "demonstrators" ],
"answer": "councilmen"
}
```
### Data Fields
The data fields are the same among all splits.
#### default
- `sentence`: a `string` feature.
- `question`: a `string` feature.
- `options`: a `list` of `string` features.
- `answer`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-------|----:|---------:|---:|
|default| 80| 82| 100|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
Our code for running decaNLP has been open sourced under BSD-3-Clause.
We chose to restrict decaNLP to datasets that were free and publicly accessible for research, but you should check their individual terms if you deviate from this use case.
From the [Winograd Schema Challenge](https://cs.nyu.edu/~davise/papers/WinogradSchemas/WS.html):
> Both versions of the collections are licenced under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).
### Citation Information
If you use this in your work, please cite:
```
@article{McCann2018decaNLP,
title={The Natural Language Decathlon: Multitask Learning as Question Answering},
author={Bryan McCann and Nitish Shirish Keskar and Caiming Xiong and Richard Socher},
journal={arXiv preprint arXiv:1806.08730},
year={2018}
}
```
### Contributions
Thanks to [@thomwolf](https://github.com/thomwolf), [@lewtun](https://github.com/lewtun), [@ghomasHudson](https://github.com/ghomasHudson), [@lhoestq](https://github.com/lhoestq) for adding this dataset.
|
zest | 2022-11-18T22:05:40.000Z | [
"task_categories:question-answering",
"task_categories:token-classification",
"task_ids:closed-domain-qa",
"task_ids:extractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"output-structure",
"yes-no-qa",
"arxiv:2011.08115",
"region:us"
] | null | ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of
the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include
classification, typed entity extraction and relationship extraction, and each task is paired with 20 different
annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize
in five different ways. | @inproceedings{weller-etal-2020-learning,
title = "Learning from Task Descriptions",
author = "Weller, Orion and
Lourie, Nicholas and
Gardner, Matt and
Peters, Matthew",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.105",
pages = "1361--1375",
abstract = "Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this frame- work with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model{'}s ability to solve each task. Moreover, the dataset{'}s structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12% on ZEST, leaving a significant challenge for NLP researchers.",
} | null | 1 | 180 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- question-answering
- token-classification
task_ids:
- closed-domain-qa
- extractive-qa
paperswithcode_id: zest
pretty_name: ZEST
tags:
- output-structure
- yes-no-qa
dataset_info:
features:
- name: task_id
dtype: string
- name: question
dtype: string
- name: generalization_type
dtype: string
- name: derives_from
sequence: string
- name: domain
dtype: string
- name: context
dtype: string
- name: answer
sequence: string
- name: all_answers
sequence: string
splits:
- name: train
num_bytes: 9588987
num_examples: 10766
- name: validation
num_bytes: 2056804
num_examples: 2280
- name: test
num_bytes: 9280845
num_examples: 11980
download_size: 5796188
dataset_size: 20926636
---
# Dataset Card for "ZEST: ZEroShot learning from Task descriptions"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://allenai.org/data/zest
- **Repository:** https://github.com/allenai/zest
- **Paper:** https://arxiv.org/abs/2011.08115
- **Leaderboard:** https://leaderboard.allenai.org/zest/submissions/public
- **Point of Contact:**
### Dataset Summary
ZEST tests whether NLP systems can perform unseen tasks in a zero-shot way, given a natural language description of
the task. It is an instantiation of our proposed framework "learning from task descriptions". The tasks include
classification, typed entity extraction and relationship extraction, and each task is paired with 20 different
annotated (input, output) examples. ZEST's structure allows us to systematically test whether models can generalize
in five different ways.
### Supported Tasks and Leaderboards
A [leaderboard](https://leaderboard.allenai.org/zest/submissions/public) is included with accepatbility metrics for
each of the four generalization types outlined in the paper. The metrics are novel acceptability metrics also
proposed by the authors.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
To evaluate the ability of a model to generalize to unseen tasks based only on a task description in a zero-shot
manner.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
Mechanical Turk crowdsource workers.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
Mechanical Turk crowdsource workers.
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
The dataset emphasizes a model's ability to generalize to unseen tasks with only a natural language description of
the task. The long-term vision of this type of evaluation is to facilitate the creation of models which can perform
arbitrary tasks with only a prompt from a non-technical user. This could broaden the frontier of what a user can
ask something like a chatbot to do for them, but it is unclear how restrictions would be put in place to prevent
users from prompting a system to perform unethical tasks.
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This dataset is licensed under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
### Citation Information
```
@inproceedings{weller-etal-2020-learning,
title = "Learning from Task Descriptions",
author = "Weller, Orion and
Lourie, Nicholas and
Gardner, Matt and
Peters, Matthew",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.emnlp-main.105",
pages = "1361--1375",
abstract = "Typically, machine learning systems solve new tasks by training on thousands of examples. In contrast, humans can solve new tasks by reading some instructions, with perhaps an example or two. To take a step toward closing this gap, we introduce a framework for developing NLP systems that solve new tasks after reading their descriptions, synthesizing prior work in this area. We instantiate this frame- work with a new English language dataset, ZEST, structured for task-oriented evaluation on unseen tasks. Formulating task descriptions as questions, we ensure each is general enough to apply to many possible inputs, thus comprehensively evaluating a model{'}s ability to solve each task. Moreover, the dataset{'}s structure tests specific types of systematic generalization. We find that the state-of-the-art T5 model achieves a score of 12% on ZEST, leaving a significant challenge for NLP researchers.",
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. |
yxchar/sciie-tlm | 2021-11-05T02:04:05.000Z | [
"region:us"
] | yxchar | null | null | null | 0 | 180 | Entry not found |
reasoning-machines/gsm-hard | 2023-01-17T03:21:10.000Z | [
"task_categories:text2text-generation",
"language_creators:crowdsourced",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:unknown",
"source_datasets:gsm8k (https://huggingface.co/datasets/gsm8k)",
"language:code",
"license:mit",
"math_reasoning",
"symbolic_reasoning",
"arxiv:2211.10435",
"region:us"
] | reasoning-machines | null | null | null | 12 | 180 | ---
annotations_creators: []
language_creators:
- crowdsourced
- expert-generated
language:
- code
license:
- mit
multilinguality:
- monolingual
size_categories:
- unknown
source_datasets:
- gsm8k (https://huggingface.co/datasets/gsm8k)
task_categories:
- text2text-generation
task_ids: []
pretty_name: gsm-hard
tags:
- math_reasoning
- symbolic_reasoning
---
## Dataset Description
- **Repository:** https://reasonwithpal.com/
- **Paper:** [PaL: Program-Aided Language Model](https://arxiv.org/abs/2211.10435)
### Dataset Summary
This is the harder version of gsm8k math reasoning dataset (https://huggingface.co/datasets/gsm8k).
We construct this dataset by replacing the numbers in the questions of GSM8K with larger numbers that are less common.
### Supported Tasks and Leaderboards
This dataset is used to evaluate math reasoning
### Languages
English - Numbers
## Dataset Structure
```python
dataset = load_dataset("reasoning-machines/gsm-hard")
DatasetDict({
train: Dataset({
features: ['input', 'code', 'target'],
num_rows: 1319
})
})
```
### Data Fields
train/dev/test:
- input: The question
- code: The corresponding code solution to the question
- target: The answer
### Citation Information
```
@article{gao2022pal,
title={PAL: Program-aided Language Models},
author={Gao, Luyu and Madaan, Aman and Zhou, Shuyan and Alon, Uri and Liu, Pengfei and Yang, Yiming and Callan, Jamie and Neubig, Graham},
journal={arXiv preprint arXiv:2211.10435},
year={2022}
}
``` |
AnonymousSub/MedQuAD_47441_Question_Answer_Pairs | 2023-03-09T15:02:29.000Z | [
"region:us"
] | AnonymousSub | null | null | null | 4 | 180 | ---
dataset_info:
features:
- name: Questions
dtype: string
- name: Answers
dtype: string
splits:
- name: train
num_bytes: 24216623
num_examples: 47441
download_size: 9258859
dataset_size: 24216623
---
# Dataset Card for "MedQuAD_47441_Question_Answer_Pairs"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
germank/shp_with_features_20k_flan_t5_large | 2023-05-15T08:49:22.000Z | [
"region:us"
] | germank | null | null | null | 0 | 180 | ---
dataset_info:
features:
- name: post_id
dtype: string
- name: domain
dtype: string
- name: upvote_ratio
dtype: float64
- name: history
dtype: string
- name: c_root_id_A
dtype: string
- name: c_root_id_B
dtype: string
- name: created_at_utc_A
dtype: int64
- name: created_at_utc_B
dtype: int64
- name: score_A
dtype: int64
- name: score_B
dtype: int64
- name: human_ref_A
dtype: string
- name: human_ref_B
dtype: string
- name: labels
dtype: int64
- name: seconds_difference
dtype: float64
- name: score_ratio
dtype: float64
- name: helpfulness_A
dtype: float64
- name: helpfulness_B
dtype: float64
- name: specificity_A
dtype: float64
- name: specificity_B
dtype: float64
- name: intent_A
dtype: float64
- name: intent_B
dtype: float64
- name: factuality_A
dtype: float64
- name: factuality_B
dtype: float64
- name: easy-to-understand_A
dtype: float64
- name: easy-to-understand_B
dtype: float64
- name: relevance_A
dtype: float64
- name: relevance_B
dtype: float64
- name: readability_A
dtype: float64
- name: readability_B
dtype: float64
- name: enough-detail_A
dtype: float64
- name: enough-detail_B
dtype: float64
- name: biased:_A
dtype: float64
- name: biased:_B
dtype: float64
- name: fail-to-consider-individual-preferences_A
dtype: float64
- name: fail-to-consider-individual-preferences_B
dtype: float64
- name: repetetive_A
dtype: float64
- name: repetetive_B
dtype: float64
- name: fail-to-consider-context_A
dtype: float64
- name: fail-to-consider-context_B
dtype: float64
- name: too-long_A
dtype: float64
- name: too-long_B
dtype: float64
- name: __index_level_0__
dtype: int64
- name: log_score_A
dtype: float64
- name: log_score_B
dtype: float64
splits:
- name: test
num_bytes: 20659940
num_examples: 9459
- name: train
num_bytes: 20707062
num_examples: 9459
download_size: 23927350
dataset_size: 41367002
---
# Dataset Card for "shp_with_features_20k_flan_t5_large"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
abacusai/WikiQA-Free_Form_QA | 2023-07-27T14:37:54.000Z | [
"region:us"
] | abacusai | null | null | null | 7 | 180 | ---
configs:
- config_name: default
data_files:
- split: 2k
path: data/2k-*
- split: 4k
path: data/4k-*
- split: 8k
path: data/8k-*
- split: 16k
path: data/16k-*
dataset_info:
features:
- name: conversations
list:
- name: from
dtype: string
- name: tok_len
dtype: int64
- name: value
dtype: string
splits:
- name: 2k
num_bytes: 3555934
num_examples: 600
- name: 4k
num_bytes: 6926324
num_examples: 600
- name: 8k
num_bytes: 13605196
num_examples: 600
- name: 16k
num_bytes: 24856440
num_examples: 600
download_size: 10741984
dataset_size: 48943894
---
# Dataset Card for "WikiQA-Free_Form_QA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
kelm | 2022-11-18T20:16:37.000Z | [
"task_categories:other",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"data-to-text-generation",
"arxiv:2010.12688",
"region:us"
] | null | Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into
a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.
The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations. | @misc{agarwal2020large,
title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},
author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
year={2020},
eprint={2010.12688},
archivePrefix={arXiv},
primaryClass={cs.CL}
} | null | 6 | 179 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: kelm
pretty_name: Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)
tags:
- data-to-text-generation
dataset_info:
features:
- name: triple
dtype: string
- name: sentence
dtype: string
splits:
- name: train
num_bytes: 1343187306
num_examples: 6371131
- name: validation
num_bytes: 167790917
num_examples: 796471
- name: test
num_bytes: 167921750
num_examples: 796493
download_size: 1631259869
dataset_size: 1678899973
---
# Dataset Card for Corpus for Knowledge-Enhanced Language Model Pre-training (KELM)
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/google-research-datasets/KELM-corpus
- **Repository:** https://github.com/google-research-datasets/KELM-corpus
- **Paper:** https://arxiv.org/abs/2010.12688
- **Leaderboard:**
- **Point of Contact:**
### Dataset Summary
Data-To-Text Generation involves converting knowledge graph (KG) triples of the form (subject, relation, object) into
a natural language sentence(s). This dataset consists of English KG data converted into paired natural language text.
The generated corpus consists of ∼18M sentences spanning ∼45M triples with ∼1500 distinct relations.
### Supported Tasks and Leaderboards
The intended task is data-to-text generation, taking in a knowledge graph tuple and generating a natural language
representation from it. Specifically, the data is in the format the authors used to train a seq2seq language model
with the tuples concatenated into a single sequence.
### Languages
The dataset is in English.
## Dataset Structure
### Data Instances
Each instance consists of one KG triple paired with corresponding natural language.
### Data Fields
- `triple`: Wikipedia triples of the form `<subject> <relation> <object>` where some subjects have multiple
relations, e.g. `<subject> <relation1> <object1> <relation2> <object2> <relation3> <object3>`. For more details on
how these relations are grouped, please refer to the paper.
- `sentence`: The corresponding Wikipedia sentence.
### Data Splits
The dataset includes a pre-determined train, validation, and test split.
## Dataset Creation
### Curation Rationale
The goal of the dataset's curation and the associated modeling work discussed in the paper is to be able to generate
natural text from a knowledge graph.
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
The data is sourced from English Wikipedia and it's associated knowledge graph.
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
From the paper:
> Wikipedia has documented ideological, gender6, and racial biases in its text. While the KELM corpus may still
contain some of these biases, certain types of biases may be reduced.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
This dataset has been released under the [CC BY-SA 2.0 license](https://creativecommons.org/licenses/by-sa/2.0/).
### Citation Information
```
@misc{agarwal2020large,
title={Large Scale Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training},
author={Oshin Agarwal and Heming Ge and Siamak Shakeri and Rami Al-Rfou},
year={2020},
eprint={2010.12688},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
### Contributions
Thanks to [@joeddav](https://github.com/joeddav) for adding this dataset. |
gia-project/gia-dataset | 2023-09-05T06:36:39.000Z | [
"task_categories:reinforcement-learning",
"task_categories:text-generation",
"task_categories:question-answering",
"annotations_creators:found",
"annotations_creators:machine-generated",
"source_datasets:conceptual-captions",
"source_datasets:ok-vqa",
"source_datasets:oscar",
"license:apache-2.0",
"imitation-learning",
"reinforcement-learning",
"text-generation",
"question-answering",
"generalist-agent",
"arxiv:2303.03915",
"region:us"
] | gia-project | GIA dataset. | null | null | 1 | 179 | ---
license: apache-2.0
tags:
- imitation-learning
- reinforcement-learning
- text-generation
- question-answering
- generalist-agent
annotations_creators:
- found
- machine-generated
pretty_name: GIA-dataset
size_categories:
- {number_of_elements_in_dataset} # Example: n<1K, 100K<n<1M, …
source_datasets:
- conceptual-captions
- ok-vqa
- oscar
task_categories:
- reinforcement-learning # Atari, Mujoco, Metaworld, BabyAI
- text-generation # OSCAR
- question-answering # OK-VQA, conceptual-captions
dataset_info:
features:
- name: text
dtype: string
- name: images
dtype: image
- name: image_observations
sequence:
dtype: image
- name: text_observations
sequence:
dtype: string
- name: discrete_observations
sequence:
sequence:
dtype: int64
- name: continuous_observations
sequence:
sequence:
dtype: float32
- name: continuous_actions
sequence:
sequence:
dtype: float32
- name: discrete_actions
sequence:
dtype: int64
- name: rewards
sequence:
dtype: float32
splits:
- name: train
num_examples:
- name: test
num_examples:
download_size:
dataset_size:
---
# GIA Dataset
## Dataset Description
The GIA dataset combines a wide range of individual datasets. It includes expert demonstrations by expert RL agents, image and caption pairs, textual data and more. The GIA dataset is part of the GIA project, which aims to build a multimodal generalist agent.
### Usage
```python
>>> from datasets import load_dataset
>>> dataset = load_dataset("gia-project/gia-dataset", "metaworld-assembly")
>>> first_episode = dataset["train"][0]
>>> first_episode.keys()
dict_keys(['continuous_observations', 'continuous_actions', 'rewards'])
>>> len(first_episode["rewards"])
500
>>> first_episode["continuous_actions"][0]
[6.459120273590088, 2.2422609329223633, -5.914587020874023, -19.799840927124023]
```
## Dataset Structure
### Data Instances
<details>
<summary>Click to expand the score information for each task</summary>
The following table presents a comparative analysis of scores across various domains and tasks. The scores highlight the performance difference between a random agent and the episodes recorded in our dataset.
| Task | Random Agent Score | Dataset Episode Score |
| ----------------------------------- | :-----------------: | :-------------------: |
| **Atari** | | |
| atari-alien | 205.50 ± 111.97 | 16912.50 ± 7087.42 |
| atari-amidar | 2.38 ± 2.50 | 2164.71 ± 1229.47 |
| atari-assault | 262.50 ± 89.61 | 15699.12 ± 9572.12 |
| atari-asterix | 213.50 ± 110.87 | 3699.62 ± 2421.30 |
| atari-asteroids | 856.40 ± 434.32 | 177011.05 ± 35334.20 |
| atari-atlantis | 17764.00 ± 6662.43 | 320679.59 ± 418247.37 |
| atari-bankheist | 13.40 ± 11.07 | 1322.43 ± 60.84 |
| atari-battlezone | 2170.00 ± 2121.58 | 295592.59 ± 161960.96 |
| atari-beamrider | 357.28 ± 143.97 | 29589.35 ± 16132.96 |
| atari-berzerk | 160.10 ± 118.87 | 57085.26 ± 13104.53 |
| atari-bowling | 23.81 ± 6.07 | 20.40 ± 7.29 |
| atari-boxing | 0.52 ± 4.37 | 97.97 ± 3.77 |
| atari-breakout | 1.24 ± 1.30 | 702.97 ± 203.62 |
| atari-centipede | 2150.06 ± 1113.28 | 11624.29 ± 4918.34 |
| atari-choppercommand | 875.00 ± 416.98 | 90990.62 ± 270876.93 |
| atari-crazyclimber | 7376.00 ± 2253.09 | 179296.94 ± 39862.06 |
| atari-defender | 3417.50 ± 1443.41 | 351958.33 ± 40466.82 |
| atari-demonattack | 165.55 ± 92.93 | 92195.25 ± 26174.79 |
| atari-doubledunk | -18.54 ± 3.07 | 20.94 ± 3.65 |
| atari-enduro | 0.00 ± 0.00 | 2292.22 ± 147.54 |
| atari-fishingderby | -93.90 ± 3.51 | 7.18 ± 25.06 |
| atari-freeway | 0.01 ± 0.10 | 33.88 ± 0.35 |
| atari-frostbite | 67.60 ± 37.61 | 13196.12 ± 4341.00 |
| atari-gopher | 319.40 ± 228.24 | 81676.15 ± 46329.48 |
| atari-gravitar | 188.50 ± 203.33 | 3986.57 ± 1729.05 |
| atari-hero | 475.25 ± 894.95 | 44677.35 ± 1754.42 |
| atari-icehockey | -9.83 ± 3.24 | 25.17 ± 5.79 |
| atari-jamesbond | 28.50 ± 45.42 | 27786.89 ± 33819.20 |
| atari-kangaroo | 52.00 ± 108.15 | 574.05 ± 636.94 |
| atari-krull | 1754.00 ± 583.56 | 11439.83 ± 1218.34 |
| atari-kungfumaster | 390.00 ± 359.03 | 32392.81 ± 10006.55 |
| atari-montezumarevenge | 0.00 ± 0.00 | 393.53 ± 50.45 |
| atari-mspacman | 246.40 ± 121.22 | 6896.08 ± 2031.99 |
| atari-namethisgame | 2447.40 ± 888.97 | 22991.18 ± 2473.15 |
| atari-phoenix | 776.80 ± 635.86 | 424583.16 ± 97649.17 |
| atari-pitfall | -259.75 ± 384.26 | -1.45 ± 4.50 |
| atari-pong | -20.22 ± 0.95 | 20.99 ± 0.18 |
| atari-privateeye | 41.65 ± 191.83 | 100.00 ± 0.00 |
| atari-qbert | 164.25 ± 151.79 | 42971.37 ± 85070.72 |
| atari-riverraid | 1474.40 ± 314.59 | 14800.94 ± 7924.56 |
| atari-roadrunner | 11.00 ± 42.18 | 77942.80 ± 6088.62 |
| atari-robotank | 1.87 ± 1.59 | 80.51 ± 13.28 |
| atari-seaquest | 73.20 ± 57.91 | 2597.34 ± 386.09 |
| atari-skiing | -16299.52 ± 1850.70 | -10738.06 ± 111.13 |
| atari-solaris | 2360.40 ± 1852.03 | 1353.68 ± 516.96 |
| atari-spaceinvaders | 137.20 ± 95.82 | 29425.29 ± 23623.89 |
| atari-stargunner | 652.00 ± 312.24 | 360588.57 ± 49207.71 |
| atari-surround | -9.99 ± 0.10 | 9.39 ± 0.85 |
| atari-tennis | -23.95 ± 0.22 | 11.11 ± 7.57 |
| atari-timepilot | 3396.00 ± 2128.85 | 69583.33 ± 29838.67 |
| atari-tutankham | 12.73 ± 17.40 | 291.16 ± 30.37 |
| atari-upndown | 358.90 ± 380.11 | 429418.33 ± 7187.43 |
| atari-venture | 0.00 ± 0.00 | 0.00 ± 0.00 |
| atari-videopinball | 23917.17 ± 19449.59 | 441507.92 ± 283264.62 |
| atari-wizardofwor | 620.00 ± 837.85 | 49333.33 ± 16157.08 |
| atari-yarsrevenge | 3503.91 ± 906.14 | 270262.86 ± 161815.96 |
| atari-zaxxon | 21.00 ± 102.27 | 73097.22 ± 14825.77 |
| **BabyAI** | | |
| babyai-action-obj-door | 0.37 ± 0.39 | 0.99 ± 0.01 |
| babyai-blocked-unlock-pickup | 0.00 ± 0.02 | 0.95 ± 0.01 |
| babyai-boss-level | 0.06 ± 0.21 | 0.94 ± 0.05 |
| babyai-boss-level-no-unlock | 0.06 ± 0.19 | 0.94 ± 0.05 |
| babyai-find-obj-s5 | 0.08 ± 0.23 | 0.95 ± 0.04 |
| babyai-go-to | 0.13 ± 0.29 | 0.92 ± 0.07 |
| babyai-go-to-door | 0.45 ± 0.38 | 0.99 ± 0.00 |
| babyai-go-to-imp-unlock | 0.08 ± 0.23 | |
| babyai-go-to-local | 0.16 ± 0.30 | 0.93 ± 0.04 |
| babyai-go-to-obj | 0.13 ± 0.27 | 0.93 ± 0.03 |
| babyai-go-to-obj-door | 0.53 ± 0.39 | 0.99 ± 0.01 |
| babyai-go-to-red-ball | 0.17 ± 0.30 | 0.93 ± 0.04 |
| babyai-go-to-red-ball-grey | 0.12 ± 0.27 | 0.92 ± 0.05 |
| babyai-go-to-red-ball-no-dists | 0.14 ± 0.28 | 0.93 ± 0.03 |
| babyai-go-to-red-blue-ball | 0.12 ± 0.27 | 0.92 ± 0.05 |
| babyai-go-to-seq | 0.08 ± 0.23 | 0.94 ± 0.05 |
| babyai-key-corridor | 0.00 ± 0.00 | 0.91 ± 0.01 |
| babyai-key-in-box | 0.01 ± 0.06 | |
| babyai-mini-boss-level | 0.07 ± 0.21 | 0.89 ± 0.10 |
| babyai-move-two-across-s8n9 | 0.00 ± 0.00 | 0.96 ± 0.01 |
| babyai-one-room-s8 | 0.08 ± 0.21 | 0.92 ± 0.03 |
| babyai-open | 0.10 ± 0.24 | 0.95 ± 0.05 |
| babyai-open-door | 0.23 ± 0.34 | 0.99 ± 0.00 |
| babyai-open-doors-order-n4 | 0.16 ± 0.30 | 0.99 ± 0.01 |
| babyai-open-red-door | 0.08 ± 0.21 | 0.92 ± 0.03 |
| babyai-open-two-doors | 0.08 ± 0.20 | 0.98 ± 0.00 |
| babyai-pickup | 0.08 ± 0.22 | 0.92 ± 0.07 |
| babyai-pickup-above | 0.02 ± 0.09 | 0.91 ± 0.07 |
| babyai-pickup-dist | 0.10 ± 0.24 | 0.86 ± 0.21 |
| babyai-pickup-loc | 0.08 ± 0.23 | 0.91 ± 0.04 |
| babyai-synth | 0.11 ± 0.26 | 0.93 ± 0.06 |
| babyai-synth-loc | 0.13 ± 0.29 | 0.94 ± 0.06 |
| babyai-synth-seq | 0.07 ± 0.20 | 0.95 ± 0.04 |
| babyai-unblock-pickup | 0.08 ± 0.22 | 0.91 ± 0.08 |
| babyai-unlock | 0.03 ± 0.15 | |
| babyai-unlock-local | 0.01 ± 0.09 | 0.98 ± 0.01 |
| babyai-unlock-pickup | 0.00 ± 0.00 | 0.75 ± 0.04 |
| babyai-unlock-to-unlock | 0.00 ± 0.00 | |
| **MetaWorld** | | |
| metaworld-assembly | 45.30 ± 4.13 | 245.99 ± 3.50 |
| metaworld-basketball | 2.81 ± 1.24 | 627.99 ± 1.98 |
| metaworld-bin-picking | 1.89 ± 0.45 | 425.58 ± 101.86 |
| metaworld-box-close | 76.39 ± 17.91 | 512.49 ± 107.81 |
| metaworld-button-press | 31.73 ± 5.20 | 643.10 ± 12.85 |
| metaworld-button-press-topdown | 28.97 ± 10.37 | 490.18 ± 27.21 |
| metaworld-button-press-topdown-wall | 29.04 ± 10.52 | 497.19 ± 31.37 |
| metaworld-button-press-wall | 8.98 ± 3.99 | 675.41 ± 15.04 |
| metaworld-coffee-button | 31.72 ± 6.36 | 731.08 ± 29.34 |
| metaworld-coffee-pull | 4.09 ± 0.38 | 259.86 ± 88.48 |
| metaworld-coffee-push | 4.17 ± 0.76 | 496.78 ± 118.20 |
| metaworld-dial-turn | 29.64 ± 16.67 | 793.56 ± 80.06 |
| metaworld-disassemble | 40.31 ± 7.53 | 42.83 ± 6.30 |
| metaworld-door-close | 5.30 ± 1.33 | 529.75 ± 27.24 |
| metaworld-door-lock | 112.35 ± 28.63 | 811.52 ± 34.07 |
| metaworld-door-open | 56.37 ± 11.23 | 581.94 ± 19.67 |
| metaworld-door-unlock | 94.17 ± 15.56 | 802.88 ± 17.05 |
| metaworld-drawer-close | 116.73 ± 253.11 | 867.92 ± 4.48 |
| metaworld-drawer-open | 126.85 ± 25.22 | 492.99 ± 2.52 |
| metaworld-faucet-close | 253.12 ± 22.94 | 753.92 ± 13.42 |
| metaworld-faucet-open | 244.10 ± 23.25 | 705.76 ± 7.15 |
| metaworld-hammer | 95.33 ± 9.02 | 693.17 ± 34.62 |
| metaworld-hand-insert | 2.75 ± 3.53 | 740.53 ± 36.69 |
| metaworld-handle-press | 80.41 ± 110.19 | 855.91 ± 72.75 |
| metaworld-handle-press-side | 57.00 ± 39.47 | 861.12 ± 20.01 |
| metaworld-handle-pull | 10.34 ± 13.54 | 669.35 ± 24.81 |
| metaworld-handle-pull-side | 2.13 ± 2.76 | 384.65 ± 102.89 |
| metaworld-lever-pull | 60.31 ± 15.77 | 612.04 ± 38.85 |
| metaworld-peg-insert-side | 1.71 ± 0.36 | 315.23 ± 140.07 |
| metaworld-peg-unplug-side | 4.75 ± 2.83 | 456.12 ± 81.65 |
| metaworld-pick-out-of-hole | 1.51 ± 0.24 | 219.61 ± 88.85 |
| metaworld-pick-place | 1.61 ± 0.99 | 419.10 ± 98.19 |
| metaworld-pick-place-wall | 0.00 ± 0.01 | 450.57 ± 64.10 |
| metaworld-plate-slide | 74.64 ± 13.84 | 527.01 ± 155.34 |
| metaworld-plate-slide-back | 33.47 ± 11.22 | 718.22 ± 87.41 |
| metaworld-plate-slide-back-side | 34.34 ± 11.53 | 729.61 ± 69.15 |
| metaworld-plate-slide-side | 22.61 ± 17.36 | 662.81 ± 102.81 |
| metaworld-push | 5.51 ± 2.43 | 750.57 ± 43.98 |
| metaworld-push-back | 1.21 ± 0.16 | 85.05 ± 107.12 |
| metaworld-push-wall | 6.13 ± 3.17 | 748.87 ± 10.62 |
| metaworld-reach | 149.67 ± 44.70 | 681.37 ± 133.68 |
| metaworld-reach-wall | 143.26 ± 36.56 | 746.12 ± 104.19 |
| metaworld-shelf-place | 0.00 ± 0.01 | 241.34 ± 24.60 |
| metaworld-soccer | 5.66 ± 4.61 | 375.15 ± 140.24 |
| metaworld-stick-pull | 2.64 ± 1.41 | 523.55 ± 18.94 |
| metaworld-stick-push | 2.81 ± 1.04 | 627.95 ± 10.20 |
| metaworld-sweep | 11.23 ± 7.28 | 494.85 ± 43.29 |
| metaworld-sweep-into | 12.55 ± 10.72 | 799.21 ± 19.07 |
| metaworld-window-close | 57.46 ± 7.11 | 591.30 ± 38.63 |
| metaworld-window-open | 43.36 ± 2.09 | 590.82 ± 57.08 |
| **MuJoCo** | | |
| mujoco-ant | -59.95 ± 99.62 | 5846.42 ± 942.55 |
| mujoco-doublependulum | 57.46 ± 17.54 | 9338.69 ± 352.61 |
| mujoco-halfcheetah | -284.97 ± 79.83 | 7437.77 ± 173.30 |
| mujoco-hopper | 18.38 ± 17.09 | 1858.73 ± 534.07 |
| mujoco-humanoid | 122.02 ± 35.28 | 6281.02 ± 1795.84 |
| mujoco-pendulum | 6.07 ± 3.47 | 475.40 ± 178.96 |
| mujoco-pusher | -149.69 ± 7.41 | -25.21 ± 6.66 |
| mujoco-reacher | -43.00 ± 3.91 | -5.68 ± 2.53 |
| mujoco-standup | 33135.75 ± 2481.89 | 273574.16 ± 85253.26 |
| mujoco-swimmer | 0.80 ± 10.71 | 92.18 ± 4.44 |
| mujoco-walker | 2.68 ± 6.06 | 4631.22 ± 1059.01 |
</details>
### Data Fields
- `text`: a `string` feature
- `images`: a `image` feature
- `image_observations` : a `Sequence(image)` feature
- `text_observations` : a `Sequence(string)` feature
- `discrete_observations`: a `Sequence(Sequence(int64))` feature
- `continuous_observations`: a `Sequence(Sequence(float32))` feature
- `continuous_actions`: a `Sequence(Sequence(float32))` feature
- `discrete_actions`: a `Sequence(int64)` feature
- `rewards`: a `Sequence(float32)` feature
### Data Splits
- `train`: `` examples
- `test`: `` examples
## Dataset Creation
This section describes how our dataset was created. We specifically detail how data for each domain and task were generated. The generation scripts are available in the [GIA repository](https://github.com/huggingface/gia). For RL tasks, we trained one agent per task using the [Sample Factory](https://www.samplefactory.dev). Then we used the trained agent to generate episodes.
### Atari
We used the 57 [ALE/Atari](https://github.com/Farama-Foundation/Arcade-Learning-Environment) games as our environment, configuring the following parameters for our experiments. We rendered the images in grayscale with an 84x84 pixel resolution. The agent interacted with the environment every 4 frames. Sticky actions were not used, and the raw reward (no clipping) was reported. Episodes were stored as complete, i.e. with no termination on life loss.
### BabyAI
We used BabyAI's implementation from [Minigrid](https://github.com/Farama-Foundation/Minigrid).
We reused the [bot agent](https://github.com/mila-iqia/babyai) provided with BabyAI's paper and adapted it to the new Minigrid API.
Using the bot, we generated 100.000 episodes for each of the 39 tasks of [Minigrid's BabyAI](https://minigrid.farama.org/environments/babyai/) and stored for each step:
- the mission: str
- the concatenation of the symbolic observation flattened and the direction: Array of integers of size (147,)
- the action: integer
- the reward: float
### Conceptual Captions
The [Conceptual Captions](https://github.com/google-research-datasets/conceptual-captions/tree/master) dataset, offered by Google LLC, comprises pairs of image links and their corresponding captions. Each image has been downloaded and, when required, resized to ensure the maximum dimension does not exceed 352 pixels.
### MetaWorld
We used the 50 tasks from [MetaWorld v2](https://github.com/Farama-Foundation/Metaworld). We constrained the episode to a duration of 100 timesteps, which is always sufficient to solve the task.
### MuJoCo
We used the 11 environments of Gymnasium MuJoCo.
### OK-VQA
The [OK-VQA](https://okvqa.allenai.org/index.html) dataset released by Kenneth Marino, Mohammad Rastegari, Ali Farhadi, Roozbeh Mottaghi was used.
The data were formatted to match Hugging Face dataset's requirements and images were resized such that the largest dimension is at most 352.
### OSCAR
We modified the "unshuffled_deduplicated_en" split of [OSCAR 2019](https://huggingface.co/datasets/oscar) dataset, initially put together by Pedro J. Ortiz, Benoît Sagot, and Laurent Romary and licensed under [CC BY 4.0](https://oscar-project.github.io/documentation/versions/oscar-2019/#license).
We cleaned and deduplicated the dataset using [the methods](https://github.com/bigscience-workshop/data-preparation/tree/main/preprocessing/training/01b_oscar_cleaning_and_filtering) and parameters used for the [ROOTS dataset](https://arxiv.org/abs/2303.03915) (Lurençon et al., 2023).
The dataset was splitted into 30 even shards each cleaned and deduplicated independently before being concatenated again.
## Considerations for Using the Data
### Known Issues
- Some BabyAI tasks are missing due to incompatibility with the training bot:
- `babyai-key-in-box`
- `babyai-go-to-imp-unlock`
- `babyai-unlock-to-unlock`
- `babyai-unlock`
- For some atari tasks, the episode is too long, causing an `OverflowError` when loading the dataset:
- `atari-enduro`
- For some tasks, although the score can be higher than the random agent, we can't consider the task as solved:
- `atari-bowling`
- `atari-privateeye`
- `atari-solaris`
- `atari-venture`
- `metaworld-bin-picking`
- `metaworld-disassemble`
- `metaworld-peg-insert-side`
- `metaworld-plate-slide`
- `metaworld-push-back`
### Future Developments
We plan to expand the dataset to include the following additional domains:
- [ ] DM Lab
- [ ] Sokoban
- [ ] Procgen
- [ ] DM Control Suite (w and w/o pixels)
## Additional Information
### Licensing Information
This dataset is release under the Apache 2.0 license.
### Citation Information
```bibtex
@misc{gallouedec2023giadataset,
title={GIA Dataset: A Multi-Modal, Multi-Task Learning Resource},
author={Gallouédec, Quentin and Beeching, Edward and Romac, Clément},
year={2023},
howpublished={\url{https://huggingface.co/datasets/gia-project/gia-dataset}},
note={Part of the GIA Project}
}
```
## Acknowledgment
We would like to extend our sincere gratitude to:
- [Shengyi Costa Huang](https://huggingface.co/vwxyzjn) for his invaluable assistance with the pretrained models used in this research
|
vjain/CBT | 2023-05-27T13:01:00.000Z | [
"license:openrail",
"region:us"
] | vjain | null | null | null | 0 | 179 | ---
license: openrail
---
|
medal | 2023-06-13T12:39:11.000Z | [
"task_categories:other",
"annotations_creators:expert-generated",
"language_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:unknown",
"disambiguation",
"region:us"
] | null | A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate | @inproceedings{wen-etal-2020-medal,
title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
author = "Wen, Zhi and
Lu, Xing Han and
Reddy, Siva",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
pages = "130--135",
abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",
} | null | 9 | 178 | ---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- other
task_ids: []
paperswithcode_id: medal
pretty_name: MeDAL
tags:
- disambiguation
dataset_info:
features:
- name: abstract_id
dtype: int32
- name: text
dtype: string
- name: location
sequence: int32
- name: label
sequence: string
splits:
- name: train
num_bytes: 3573399948
num_examples: 3000000
- name: test
num_bytes: 1190766821
num_examples: 1000000
- name: validation
num_bytes: 1191410723
num_examples: 1000000
- name: full
num_bytes: 15536883723
num_examples: 14393619
download_size: 21060929078
dataset_size: 21492461215
---
# Dataset Card for the MeDAL dataset
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Repository:** https://github.com/BruceWen120/medal
- **Paper:** https://www.aclweb.org/anthology/2020.clinicalnlp-1.15/
- **Dataset (Kaggle):** https://www.kaggle.com/xhlulu/medal-emnlp
- **Dataset (Zenodo):** https://zenodo.org/record/4265632
- **Pretrained model:** https://huggingface.co/xhlu/electra-medal
- **Leaderboard:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Dataset Summary
A large medical text dataset (14Go) curated to 4Go for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. For example, DHF can be disambiguated to dihydrofolate, diastolic heart failure, dengue hemorragic fever or dihydroxyfumarate
### Supported Tasks and Leaderboards
Medical abbreviation disambiguation
### Languages
English (en)
## Dataset Structure
Each file is a table consisting of three columns:
* text: The normalized content of an abstract
* location: The location (index) of each abbreviation that was substituted
* label: The word at that was substituted at the given location
### Data Instances
An example from the train split is:
```
{'abstract_id': 14145090,
'text': 'velvet antlers vas are commonly used in traditional chinese medicine and invigorant and contain many PET components for health promotion the velvet antler peptide svap is one of active components in vas based on structural study the svap interacts with tgfβ receptors and disrupts the tgfβ pathway we hypothesized that svap prevents cardiac fibrosis from pressure overload by blocking tgfβ signaling SDRs underwent TAC tac or a sham operation T3 one month rats received either svap mgkgday or vehicle for an additional one month tac surgery induced significant cardiac dysfunction FB activation and fibrosis these effects were improved by treatment with svap in the heart tissue tac remarkably increased the expression of tgfβ and connective tissue growth factor ctgf ROS species C2 and the phosphorylation C2 of smad and ERK kinases erk svap inhibited the increases in reactive oxygen species C2 ctgf expression and the phosphorylation of smad and erk but not tgfβ expression in cultured cardiac fibroblasts angiotensin ii ang ii had similar effects compared to tac surgery such as increases in αsmapositive CFs and collagen synthesis svap eliminated these effects by disrupting tgfβ IB to its receptors and blocking ang iitgfβ downstream signaling these results demonstrated that svap has antifibrotic effects by blocking the tgfβ pathway in CFs',
'location': [63],
'label': ['transverse aortic constriction']}
```
### Data Fields
The column types are:
* text: content of the abstract as a string
* location: index of the substitution as an integer
* label: substitued word as a string
### Data Splits
The following files are present:
* `full_data.csv`: The full dataset with all 14M abstracts.
* `train.csv`: The subset used to train the baseline and proposed models.
* `valid.csv`: The subset used to validate the model during training for hyperparameter selection.
* `test.csv`: The subset used to evaluate the model and report the results in the tables.
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
The original dataset was retrieved and modified from the [NLM website](https://www.nlm.nih.gov/databases/download/pubmed_medline.html).
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
Details on how the abbreviations were created can be found in section 2.2 (Dataset Creation) of the [ACL ClinicalNLP paper](https://aclanthology.org/2020.clinicalnlp-1.15.pdf).
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
Since the abstracts are written in English, the data is biased towards anglo-centric medical research. If you plan to use a model pre-trained on this dataset for a predominantly non-English community, it is important to verify whether there are negative biases present in your model, and ensure that they are correctly mitigated. For instance, you could fine-tune your dataset on a multilingual medical disambiguation dataset, or collect a dataset specific to your use case.
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
The ELECTRA model is licensed under [Apache 2.0](https://github.com/google-research/electra/blob/master/LICENSE). The license for the libraries used in this project (`transformers`, `pytorch`, etc.) can be found in their respective GitHub repository. Our model is released under a MIT license.
The original dataset was retrieved and modified from the [NLM website](https://www.nlm.nih.gov/databases/download/pubmed_medline.html). By using this dataset, you are bound by the [terms and conditions](https://www.nlm.nih.gov/databases/download/terms_and_conditions_pubmed.html) specified by NLM:
> INTRODUCTION
>
> Downloading data from the National Library of Medicine FTP servers indicates your acceptance of the following Terms and Conditions: No charges, usage fees or royalties are paid to NLM for this data.
>
> MEDLINE/PUBMED SPECIFIC TERMS
>
> NLM freely provides PubMed/MEDLINE data. Please note some PubMed/MEDLINE abstracts may be protected by copyright.
>
> GENERAL TERMS AND CONDITIONS
>
> * Users of the data agree to:
> * acknowledge NLM as the source of the data by including the phrase "Courtesy of the U.S. National Library of Medicine" in a clear and conspicuous manner,
> * properly use registration and/or trademark symbols when referring to NLM products, and
> * not indicate or imply that NLM has endorsed its products/services/applications.
>
> * Users who republish or redistribute the data (services, products or raw data) agree to:
> * maintain the most current version of all distributed data, or
> * make known in a clear and conspicuous manner that the products/services/applications do not reflect the most current/accurate data available from NLM.
>
> * These data are produced with a reasonable standard of care, but NLM makes no warranties express or implied, including no warranty of merchantability or fitness for particular purpose, regarding the accuracy or completeness of the data. Users agree to hold NLM and the U.S. Government harmless from any liability resulting from errors in the data. NLM disclaims any liability for any consequences due to use, misuse, or interpretation of information contained or not contained in the data.
>
> * NLM does not provide legal advice regarding copyright, fair use, or other aspects of intellectual property rights. See the NLM Copyright page.
>
> * NLM reserves the right to change the type and format of its machine-readable data. NLM will take reasonable steps to inform users of any changes to the format of the data before the data are distributed via the announcement section or subscription to email and RSS updates.
### Citation Information
```
@inproceedings{wen-etal-2020-medal,
title = "{M}e{DAL}: Medical Abbreviation Disambiguation Dataset for Natural Language Understanding Pretraining",
author = "Wen, Zhi and
Lu, Xing Han and
Reddy, Siva",
booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.15",
pages = "130--135",
abstract = "One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets. In this work, we present MeDAL, a large medical text dataset curated for abbreviation disambiguation, designed for natural language understanding pre-training in the medical domain. We pre-trained several models of common architectures on this dataset and empirically showed that such pre-training leads to improved performance and convergence speed when fine-tuning on downstream medical tasks.",
}
```
### Contributions
Thanks to [@Narsil](https://github.com/Narsil) and [@xhlulu](https://github.com/xhlulu)) for adding this dataset. |
castorini/mr-tydi-corpus | 2022-10-12T20:25:51.000Z | [
"task_categories:text-retrieval",
"multilinguality:multilingual",
"language:ar",
"language:bn",
"language:en",
"language:fi",
"language:id",
"language:ja",
"language:ko",
"language:ru",
"language:sw",
"language:te",
"language:th",
"license:apache-2.0",
"region:us"
] | castorini | null | null | null | 2 | 178 | ---
language:
- ar
- bn
- en
- fi
- id
- fi
- ja
- ko
- ru
- sw
- te
- th
multilinguality:
- multilingual
task_categories:
- text-retrieval
license: apache-2.0
---
# Dataset Summary
Mr. TyDi is a multi-lingual benchmark dataset built on TyDi, covering eleven typologically diverse languages. It is designed for monolingual retrieval, specifically to evaluate ranking with learned dense representations.
This dataset stores documents of Mr. TyDi. To access the queries and judgments, please refer to [castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi).
# Dataset Structure
The only configuration here is the `language`. As all three folds (train, dev and test) share the same corpus, there is only one fold 'train' under each language, unlike [castorini/mr-tydi](https://huggingface.co/datasets/castorini/mr-tydi).
An example of document data entry looks as follows:
```
{
'docid': '25#0',
'title': 'Autism',
'text': 'Autism is a developmental disorder characterized by difficulties with social interaction and communication, ...'
}
```
# Load Dataset
An example to load the dataset:
```
language = 'english'
dataset = load_dataset('castorini/mr-tydi-corpus', language, 'train')
```
# Citation Information
```
@article{mrtydi,
title={{Mr. TyDi}: A Multi-lingual Benchmark for Dense Retrieval},
author={Xinyu Zhang and Xueguang Ma and Peng Shi and Jimmy Lin},
year={2021},
journal={arXiv:2108.08787},
}
``` |
bigbio/hallmarks_of_cancer | 2022-12-22T15:44:44.000Z | [
"multilinguality:monolingual",
"language:en",
"license:gpl-3.0",
"region:us"
] | bigbio | The Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication
abstracts manually annotated by experts according to a taxonomy. The taxonomy
consists of 37 classes in a hierarchy. Zero or more class labels are assigned
to each sentence in the corpus. The labels are found under the "labels"
directory, while the tokenized text can be found under "text" directory.
The filenames are the corresponding PubMed IDs (PMID). | @article{DBLP:journals/bioinformatics/BakerSGAHSK16,
author = {Simon Baker and
Ilona Silins and
Yufan Guo and
Imran Ali and
Johan H{\"{o}}gberg and
Ulla Stenius and
Anna Korhonen},
title = {Automatic semantic classification of scientific literature
according to the hallmarks of cancer},
journal = {Bioinform.},
volume = {32},
number = {3},
pages = {432--440},
year = {2016},
url = {https://doi.org/10.1093/bioinformatics/btv585},
doi = {10.1093/bioinformatics/btv585},
timestamp = {Thu, 14 Oct 2021 08:57:44 +0200},
biburl = {https://dblp.org/rec/journals/bioinformatics/BakerSGAHSK16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | null | 1 | 178 |
---
language:
- en
bigbio_language:
- English
license: gpl-3.0
multilinguality: monolingual
bigbio_license_shortname: GPL_3p0
pretty_name: Hallmarks of Cancer
homepage: https://github.com/sb895/Hallmarks-of-Cancer
bigbio_pubmed: True
bigbio_public: True
bigbio_tasks:
- TEXT_CLASSIFICATION
---
# Dataset Card for Hallmarks of Cancer
## Dataset Description
- **Homepage:** https://github.com/sb895/Hallmarks-of-Cancer
- **Pubmed:** True
- **Public:** True
- **Tasks:** TXTCLASS
The Hallmarks of Cancer (HOC) Corpus consists of 1852 PubMed publication
abstracts manually annotated by experts according to a taxonomy. The taxonomy
consists of 37 classes in a hierarchy. Zero or more class labels are assigned
to each sentence in the corpus. The labels are found under the "labels"
directory, while the tokenized text can be found under "text" directory.
The filenames are the corresponding PubMed IDs (PMID).
## Citation Information
```
@article{DBLP:journals/bioinformatics/BakerSGAHSK16,
author = {Simon Baker and
Ilona Silins and
Yufan Guo and
Imran Ali and
Johan H{"{o}}gberg and
Ulla Stenius and
Anna Korhonen},
title = {Automatic semantic classification of scientific literature
according to the hallmarks of cancer},
journal = {Bioinform.},
volume = {32},
number = {3},
pages = {432--440},
year = {2016},
url = {https://doi.org/10.1093/bioinformatics/btv585},
doi = {10.1093/bioinformatics/btv585},
timestamp = {Thu, 14 Oct 2021 08:57:44 +0200},
biburl = {https://dblp.org/rec/journals/bioinformatics/BakerSGAHSK16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
ajaykarthick/imdb-movie-reviews | 2023-02-08T21:08:35.000Z | [
"task_categories:text-classification",
"task_categories:token-classification",
"task_categories:feature-extraction",
"size_categories:10K<n<100K",
"region:us"
] | ajaykarthick | null | null | null | 1 | 178 | ---
task_categories:
- text-classification
- token-classification
- feature-extraction
pretty_name: Movie-Reviews
size_categories:
- 10K<n<100K
---
# IMDB Movie Reviews
This is a dataset for binary sentiment classification containing substantially huge data. This dataset contains a set of 50,000 highly polar movie reviews for training models for text classification tasks.
The dataset is downloaded from
https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
This data is processed and splitted into training and test datasets (0.2% test split). Training dataset contains 40000 reviews and test dataset contains 10000 reviews.
Equal distribution among the labels in both training and test dataset. in training dataset, there are 20000 records for both positive and negative classes. In test dataset, there are 5000 records both the labels.
### Citation Information
```
@InProceedings{maas-EtAl:2011:ACL-HLT2011,
author = {Maas, Andrew L. and Daly, Raymond E. and Pham, Peter T. and Huang, Dan and Ng, Andrew Y. and Potts, Christopher},
title = {Learning Word Vectors for Sentiment Analysis},
booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
month = {June},
year = {2011},
address = {Portland, Oregon, USA},
publisher = {Association for Computational Linguistics},
pages = {142--150},
url = {http://www.aclweb.org/anthology/P11-1015}
}
``` |
red_caps | 2023-01-25T14:43:07.000Z | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:cc-by-4.0",
"arxiv:2111.11431",
"region:us"
] | null | RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.
Images and captions from Reddit depict and describe a wide variety of objects and scenes.
The data is collected from a manually curated set of subreddits (350 total),
which give coarse image labels and allow steering of the dataset composition
without labeling individual instances. | @misc{desai2021redcaps,
title={RedCaps: web-curated image-text data created by the people, for the people},
author={Karan Desai and Gaurav Kaul and Zubin Aysola and Justin Johnson},
year={2021},
eprint={2111.11431},
archivePrefix={arXiv},
primaryClass={cs.CV}
} | null | 43 | 177 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: redcaps
pretty_name: RedCaps
dataset_info:
features:
- name: image_id
dtype: string
- name: author
dtype: string
- name: image_url
dtype: string
- name: raw_caption
dtype: string
- name: caption
dtype: string
- name: subreddit
dtype:
class_label:
names:
'0': abandonedporn
'1': abandoned
'2': absoluteunits
'3': airplants
'4': alltheanimals
'5': amateurphotography
'6': amateurroomporn
'7': animalporn
'8': antiques
'9': antkeeping
'10': ants
'11': aquariums
'12': architectureporn
'13': artefactporn
'14': astronomy
'15': astrophotography
'16': australiancattledog
'17': australianshepherd
'18': autumnporn
'19': averagebattlestations
'20': awwducational
'21': awwnverts
'22': axolotls
'23': backpacking
'24': backyardchickens
'25': baking
'26': ballpython
'27': barista
'28': bassfishing
'29': battlestations
'30': bbq
'31': beagle
'32': beardeddragons
'33': beekeeping
'34': beerandpizza
'35': beerporn
'36': beerwithaview
'37': beginnerwoodworking
'38': bengalcats
'39': bento
'40': bernesemountaindogs
'41': berries
'42': bettafish
'43': bicycling
'44': bikecommuting
'45': birding
'46': birdphotography
'47': birdpics
'48': birdsofprey
'49': birds
'50': blackcats
'51': blacksmith
'52': bladesmith
'53': boatporn
'54': bonsai
'55': bookporn
'56': bookshelf
'57': bordercollie
'58': bostonterrier
'59': botanicalporn
'60': breadit
'61': breakfastfood
'62': breakfast
'63': bridgeporn
'64': brochet
'65': budgetfood
'66': budgies
'67': bulldogs
'68': burgers
'69': butterflies
'70': cabinporn
'71': cactus
'72': cakedecorating
'73': cakewin
'74': cameras
'75': campingandhiking
'76': camping
'77': carnivorousplants
'78': carpentry
'79': carporn
'80': cassetteculture
'81': castiron
'82': castles
'83': casualknitting
'84': catpictures
'85': cats
'86': ceramics
'87': chameleons
'88': charcuterie
'89': cheesemaking
'90': cheese
'91': chefit
'92': chefknives
'93': chickens
'94': chihuahua
'95': chinchilla
'96': chinesefood
'97': churchporn
'98': cider
'99': cityporn
'100': classiccars
'101': cockatiel
'102': cocktails
'103': coffeestations
'104': coins
'105': cookiedecorating
'106': corgi
'107': cornsnakes
'108': cozyplaces
'109': crafts
'110': crestedgecko
'111': crochet
'112': crossstitch
'113': crows
'114': crystals
'115': cupcakes
'116': dachshund
'117': damnthatsinteresting
'118': desertporn
'119': designmyroom
'120': desksetup
'121': dessertporn
'122': dessert
'123': diy
'124': dobermanpinscher
'125': doggos
'126': dogpictures
'127': drunkencookery
'128': duck
'129': dumpsterdiving
'130': earthporn
'131': eatsandwiches
'132': embroidery
'133': entomology
'134': equestrian
'135': espresso
'136': exposureporn
'137': eyebleach
'138': f1porn
'139': farming
'140': femalelivingspace
'141': fermentation
'142': ferrets
'143': fireporn
'144': fishing
'145': fish
'146': flowers
'147': flyfishing
'148': foodporn
'149': food
'150': foraging
'151': fossilporn
'152': fountainpens
'153': foxes
'154': frenchbulldogs
'155': frogs
'156': gardening
'157': gardenwild
'158': geckos
'159': gemstones
'160': geologyporn
'161': germanshepherds
'162': glutenfree
'163': goldenretrievers
'164': goldfish
'165': gold
'166': greatpyrenees
'167': grilledcheese
'168': grilling
'169': guineapigs
'170': gunporn
'171': guns
'172': hamsters
'173': handtools
'174': healthyfood
'175': hedgehog
'176': helicopters
'177': herpetology
'178': hiking
'179': homestead
'180': horses
'181': hotpeppers
'182': houseplants
'183': houseporn
'184': husky
'185': icecreamery
'186': indoorgarden
'187': infrastructureporn
'188': insects
'189': instantpot
'190': interestingasfuck
'191': interiordesign
'192': itookapicture
'193': jellyfish
'194': jewelry
'195': kayakfishing
'196': kayaking
'197': ketorecipes
'198': knifeporn
'199': knives
'200': labrador
'201': leathercraft
'202': leopardgeckos
'203': lizards
'204': lookatmydog
'205': macarons
'206': machineporn
'207': macroporn
'208': malelivingspace
'209': mead
'210': mealprepsunday
'211': mechanicalkeyboards
'212': mechanicalpencils
'213': melts
'214': metalworking
'215': microgreens
'216': microporn
'217': mildlyinteresting
'218': mineralporn
'219': monitors
'220': monstera
'221': mostbeautiful
'222': motorcycleporn
'223': muglife
'224': mushroomgrowers
'225': mushroomporn
'226': mushrooms
'227': mycology
'228': natureisfuckinglit
'229': natureporn
'230': nebelung
'231': orchids
'232': otters
'233': outdoors
'234': owls
'235': parrots
'236': pelletgrills
'237': pens
'238': perfectfit
'239': permaculture
'240': photocritique
'241': photographs
'242': pics
'243': pitbulls
'244': pizza
'245': plantbaseddiet
'246': plantedtank
'247': plantsandpots
'248': plants
'249': pomeranians
'250': pottery
'251': pourpainting
'252': proplifting
'253': pugs
'254': pug
'255': quilting
'256': rabbits
'257': ramen
'258': rarepuppers
'259': reeftank
'260': reptiles
'261': resincasting
'262': roomporn
'263': roses
'264': rottweiler
'265': ruralporn
'266': sailing
'267': salsasnobs
'268': samoyeds
'269': savagegarden
'270': scotch
'271': seaporn
'272': seriouseats
'273': sewing
'274': sharks
'275': shiba
'276': shihtzu
'277': shrimptank
'278': siamesecats
'279': siberiancats
'280': silverbugs
'281': skyporn
'282': sloths
'283': smoking
'284': snails
'285': snakes
'286': sneakers
'287': sneks
'288': somethingimade
'289': soup
'290': sourdough
'291': sousvide
'292': spaceporn
'293': spicy
'294': spiderbro
'295': spiders
'296': squirrels
'297': steak
'298': streetphotography
'299': succulents
'300': superbowl
'301': supermodelcats
'302': sushi
'303': tacos
'304': tarantulas
'305': tastyfood
'306': teaporn
'307': tea
'308': tequila
'309': terrariums
'310': thedepthsbelow
'311': thriftstorehauls
'312': tinyanimalsonfingers
'313': tonightsdinner
'314': toolporn
'315': tools
'316': torties
'317': tortoise
'318': tractors
'319': trailrunning
'320': trains
'321': trucks
'322': turtle
'323': underwaterphotography
'324': upcycling
'325': urbanexploration
'326': urbanhell
'327': veganfoodporn
'328': veganrecipes
'329': vegetablegardening
'330': vegetarian
'331': villageporn
'332': vintageaudio
'333': vintage
'334': vinyl
'335': volumeeating
'336': watches
'337': waterporn
'338': weatherporn
'339': wewantplates
'340': wildernessbackpacking
'341': wildlifephotography
'342': wine
'343': winterporn
'344': woodcarving
'345': woodworking
'346': workbenches
'347': workspaces
'348': yarnaddicts
'349': zerowaste
- name: score
dtype: int32
- name: created_utc
dtype: timestamp[s, tz=UTC]
- name: permalink
dtype: string
- name: crosspost_parents
sequence: string
config_name: all
splits:
- name: train
num_bytes: 3378544525
num_examples: 12011121
download_size: 1061908181
dataset_size: 3378544525
---
# Dataset Card for RedCaps
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [RedCaps homepage](https://redcaps.xyz/)
- **Repository:** [RedCaps repository](https://github.com/redcaps-dataset/redcaps-downloader)
- **Paper:** [RedCaps: web-curated image-text data created by the people, for the people](https://arxiv.org/abs/2111.11431)
- **Leaderboard:**
- **Point of Contact:** [Karan Desai](mailto:kdexd@umich.edu)
### Dataset Summary
RedCaps is a large-scale dataset of 12M image-text pairs collected from Reddit.
Images and captions from Reddit depict and describe a wide variety of objects and scenes.
The data is collected from a manually curated set of subreddits (350 total),
which give coarse image labels and allow steering of the dataset composition
without labeling individual instances. RedCaps data is created *by the people, for the people* – it contains everyday things that users like to share on social media, for example hobbies (r/crafts) and pets (r/shiba). Captions often contain specific and
fine-grained descriptions (northern cardinal, taj mahal). Subreddit names provide relevant image
labels (r/shiba) even when captions may not (mlem!), and sometimes may group many visually
unrelated images through a common semantic meaning (r/perfectfit).
### Dataset Preprocessing
This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dset = load_dataset("red_caps", "rabbits_2017")
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
Some image links point to more than one image. You can process and downloaded those as follows:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import os
import re
import urllib
import PIL.Image
import datasets
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(lambda image_urls: [fetch_single_image_with_args(image_url) for image_url in image_urls], batch["image_url"]))
return batch
def process_image_urls(batch):
processed_batch_image_urls = []
for image_url in batch["image_url"]:
processed_example_image_urls = []
image_url_splits = re.findall(r"http\S+", image_url)
for image_url_split in image_url_splits:
if "imgur" in image_url_split and "," in image_url_split:
for image_url_part in image_url_split.split(","):
if not image_url_part:
continue
image_url_part = image_url_part.strip()
root, ext = os.path.splitext(image_url_part)
if not root.startswith("http"):
root = "http://i.imgur.com/" + root
root = root.split("#")[0]
if not ext:
ext = ".jpg"
ext = re.split(r"[?%]", ext)[0]
image_url_part = root + ext
processed_example_image_urls.append(image_url_part)
else:
processed_example_image_urls.append(image_url_split)
processed_batch_image_urls.append(processed_example_image_urls)
batch["image_url"] = processed_batch_image_urls
return batch
dset = load_dataset("red_caps", "rabbits_2017")
dset = dset.map(process_image_urls, batched=True, num_proc=4)
features = dset["train"].features.copy()
features["image"] = datasets.Sequence(datasets.Image())
num_threads = 20
dset = dset.map(fetch_images, batched=True, batch_size=100, features=features, fn_kwargs={"num_threads": num_threads})
```
Note that in the above code, we use the `datasets.Sequence` feature to represent a list of images for the multi-image links.
### Supported Tasks and Leaderboards
From the paper:
> We have used our dataset to train deep neural networks that perform image captioning, and
that learn transferable visual representations for a variety of downstream visual recognition tasks
(image classification, object detection, instance segmentation).
> We anticipate that the dataset could be used for a variety of vision-and-language (V&L) tasks,
such as image or text retrieval or text-to-image synthesis.
### Languages
All of the subreddits in RedCaps use English as their primary language.
## Dataset Structure
### Data Instances
Each instance in RedCaps represents a single Reddit image post:
```
{
'image_id': 'bpzj7r',
'author': 'djasz1',
'image_url': 'https://i.redd.it/ho0wntksivy21.jpg',
'raw_caption': 'Found on a friend’s property in the Keys FL. She is now happily living in my house.',
'caption': 'found on a friend's property in the keys fl. she is now happily living in my house.', 'subreddit': 3,
'score': 72,
'created_utc': datetime.datetime(2019, 5, 18, 1, 36, 41),
'permalink': '/r/airplants/comments/bpzj7r/found_on_a_friends_property_in_the_keys_fl_she_is/', 'crosspost_parents': None
}
```
### Data Fields
- `image_id`: Unique alphanumeric ID of the image post (assigned by Reddit).
- `author`: Reddit username of the image post author.
- `image_url`: Static URL for downloading the image associated with the post.
- `raw_caption`: Textual description of the image, written by the post author.
- `caption`: Cleaned version of "raw_caption" by us (see Q35).
- `subreddit`: Name of subreddit where the post was submitted.
- `score`: Net upvotes (discounting downvotes) received by the image post. This field is equal to `None` if the image post is a crosspost.
- `created_utc`: Integer time epoch (in UTC) when the post was submitted to Reddit.
- `permalink`: Partial URL of the Reddit post (https://reddit.com/<permalink>).
- `crosspost_parents`: List of parent posts. This field is optional.
### Data Splits
All the data is contained in training set. The training set has nearly 12M (12,011,111) instances.
From the paper:
> We intend our dataset to be primarily used for pre-training with one or more specific downstream task(s) in mind. Hence, all instances in our dataset would be used for training while
the validation split is derived from downstream task(s). If users require a validation split, we
recommend sampling it such that it follows the same subreddit distribution as entire dataset.
## Dataset Creation
### Curation Rationale
From the paper:
> Large datasets of image-text pairs are widely used for pre-training generic representations
that transfer to a variety of downstream vision and vision-and-language tasks. Existing public
datasets of this kind were curated from search engine results (SBU Captions [1]) or HTML
alt-text from arbitrary web pages (Conceptual Captions [2, 31]). They performed complex
data filtering to deal with noisy web data. Due to aggressive filtering, their data collection is
inefficient and diversity is artificially supressed. We argue that the quality of data depends on
its source, and the human intent behind its creation. In this work, we explore Reddit – a social
media platform, for curating high quality data. We introduce RedCaps – a large dataset of
12M image-text pairs from Reddit. While we expect the use-cases of RedCaps to be similar to
existing datasets, we discuss how Reddit as a data source leads to fast and lightweight collection,
better data quality, lets us easily steer the data distribution, and facilitates ethically responsible data curation.
### Source Data
#### Initial Data Collection and Normalization
From the paper:
> **Data Collection Pipeline**
Reddit’s uniform structure allows us to parallelize data collection as independent tasks – each task
involves collecting posts submitted to a single subreddit in one year. Our collection pipeline has three steps: (1) subreddit selection, (2) image post filtering, and (3) caption cleaning.
**Step 1**. Subreddit selection: We collect data from a manually curated set of subreddits. Subreddits
have their own rules, community norms, and moderators so curating subreddits allows us to steer the
dataset’s composition without annotating individual instances. We select subreddits with a high volume of images posts, where images tend to be photographs (rather than memes, drawings, screenshots,
etc) and post titles tend to describe image content (rather than making jokes, political commentary,
etc). We do not select any NSFW, banned, or quarantined subreddits. We want to minimize the
number of people that appear in RedCaps, so we omit subreddits whose primary purpose is to share or
comment on images of people (such as celebrity pics or user selfies). We choose subreddits focused on
general photography (r/pics, r/itookapicture), animals (r/axolotls, r/birdsofprey, r/dachshund),
plants (r/roses, r/succulents), objects (r/classiccars, r/trains, r/mechanicalkeyboards), food
(r/steak, r/macarons), scenery (r/cityporn1
, r/desertporn), or activities (r/carpentry, r/kayaking).
In total we collect data from 350 subreddits; the full list can be found in Appendix A.
**Step 2**. Image post filtering: We use Pushshift [41] and Reddit [42, 43] APIs to download all image
posts submitted to our selected subreddits from 2008–2020. Posts are collected at least six months
after their creation to let upvotes stabilize. We only collect posts with images hosted on three domains:
Reddit (i.redd.it), Imgur (i.imgur.com), and Flickr (staticflickr.com). Some image posts contain
multiple images (gallery posts) – in this case we only collect the first image and associate it with
the caption. We discard posts with < 2 upvotes to avoid unappealing content, and we discard posts
marked NSFW (by their authors or subreddit moderators) to avoid pornographic or disturbing content.
**Step 3**. Caption cleaning: We expect Reddit post titles to be less noisy than other large-scale
sources of image captions such as alt-text [2, 31], so we apply minimal text cleaning. We lowercase
captions and use ftfy [44] to remove character accents, emojis, and non-latin characters, following
[29, 35, 36]. Then we apply simple pattern matching to discard all sub-strings enclosed in brackets
((.*), [.*]). These sub-strings usually give non-semantic information: original content tags [oc],
image resolutions (800x600 px), camera specs (shot with iPhone), self-promotion [Instagram:
@user], and other references (link in comments). Finally, like [31] we replace social media
handles (words starting with ‘@’) with a [USR] token to protect user privacy and reduce redundancy.
Due to such filtering, ≈12K (0.1%) captions in our dataset are empty strings. We do not discard them,
as subreddit names alone provide meaningful supervision. Unlike CC-3M or CC-12M that discard
captions without nouns or that don’t overlap image tags, we do not discard any instances in this step.
Through this pipeline, we collect 13.4M instances from 350 subreddits. Our collection pipeline is
less resource-intensive than existing datasets – we do not require webpage crawlers, search engines,
or large databases of indexed webpages. RedCaps is easily extensible in the future by selecting more
subreddits and collecting posts from future years. Next, we perform additional filtering to mitigate
user privacy risks and harmful stereotypes in RedCaps, resulting in final size of 12M instances.
#### Who are the source language producers?
Reddit is the singular data source for RedCaps.
### Annotations
#### Annotation process
The dataset is built using fully automatic data collection pipeline which doesn't require any human annotators.
#### Who are the annotators?
The annotation process doesn't require any human annotators.
### Personal and Sensitive Information
From the paper:
> **Does the dataset relate to people?**
The dataset pertains to people in that people wrote the captions and posted images to Reddit
that we curate in RedCaps. We made specific design choices while curating RedCaps to avoid
large quantities of images containing people:
(a) We collect data from manually curated subreddits in which most contain primarily pertains
to animals, objects, places, or activities. We exclude all subreddits whose primary purpose
is to share and describe images of people (such as celebrity photos or user selfies).
(b) We use an off-the-shelf face detector to find and remove images with potential presence of
human faces. We manually checked 50K random images in RedCaps (Q16) and found 79
images with identifiable human faces – the entire dataset may have ≈19K (0.15%) images
with identifiable people. Refer Section 2.2 in the main paper.
> **Is it possible to identify one or more natural persons, either directly or indirectly (i.e., in
combination with other data) from the dataset?**
Yes, all instances in RedCaps include Reddit usernames of their post authors. This could be
used to look up the Reddit user profile, and some Reddit users may have identifying information
in their profiles. Some images may contain human faces which could be identified by
appearance. However, note that all this information is already public on Reddit, and searching it
in RedCaps is no easier than searching directly on Reddit.
> **Were the individuals in question notified about the data collection?**
No. Reddit users are anonymous by default, and are not required to share their personal contact
information (email, phone numbers, etc.). Hence, the only way to notify the authors of RedCaps
image posts is by sending them private messages on Reddit. This is practically difficult to do
manually, and will be classified as spam and blocked by Reddit if attempted to programmatically
send a templated message to millions of users.
> **Did the individuals in question consent to the collection and use of their data?**
Users did not explicitly consent to the use of their data in our dataset. However, by uploading
their data on Reddit, they consent that it would appear on the Reddit plaform and will be
accessible via the official Reddit API (which we use to collect RedCaps).
> **If consent was obtained, were the consenting individuals provided with a mechanism to
revoke their consent in the future or for certain uses?**
Users have full control over the presence of their data in our dataset. If users wish to revoke
their consent, they can delete the underlying Reddit post – it will be automatically removed
dfrom RedCaps since we distributed images as URLs. Moreover, we provide an opt-out request
form on our dataset website for anybody to request removal of an individual instance if it is
potentially harmful (e.g. NSFW, violates privacy, harmful stereotypes, etc.).
## Considerations for Using the Data
### Social Impact of Dataset
From the paper:
> **Has an analysis of the potential impact of the dataset and its use on data subjects (e.g.,
a data protection impact analysis) been conducted?**
No.
### Discussion of Biases
From the paper:
> **Harmful Stereotypes**: Another concern with
Reddit data is that images or language may represent harmful stereotypes about gender, race, or other
characteristics of people [48, 49, 51]. We select only non-NSFW subreddits with active moderation
for collecting data. This stands in contrast to less curated uses of Reddit data, such as GPT-2 [35]
whose training data includes at least 63K documents from banned or quarantined subreddits which
may contain toxic language [53]. We attempt to further reduce harmful stereotypes in two ways:
> * **NSFW images**: We use the InceptionV3 [54] model from [55] to filter images detected as porn or hentai with confidence ≥ 0.9. Similar to face filtering, we estimated precision of our filtering and estimated amount of missed detections, shown in Table 1. The model detects 87K images with low
precision (∼1%) – most detections are non-NSFW images with pink and beige hues.
> * **Potentially derogatory language**: We filter instances whose captions contain words or phrases from a common blocklist [56]. It is important to note that such coarse filtering might suppress language from marginalized groups reclaiming slurs [51]; however, as RedCaps is not intended to describe people, we believe this is a pragmatic tradeoff to avoid propagating harmful labels.
> **Reddit demographics**: Reddit’s user demographics are not representative of the population at large.
Compared to US adults, Reddit users skew male (69% vs 49%), young (58% 18-29 years old vs
22%), college educated (36% vs 28%), and politically liberal (41% vs 25%) [57]. Reddit users
are predominantly white (63%) [57], and 49% of desktop traffic to Reddit comes from the United
States [58]. All of the subreddits in RedCaps use English as their primary language. Taken together,
these demographic biases likely also bias the types of objects and places that appear in images on
Reddit, and the language used to describe these images. We do not offer explicit countermeasures to
these biases, but users of RedCaps should keep in mind that size doesn’t guarantee diversity [51].
Subtler issues may also exist, such as imbalanced representation of demographic groups [59] or
gender bias in object co-occurrence [60] or language [61]. These are hard to control in internet
data, so we release RedCaps with explicit instructions on suitable use-cases; specifically requesting models not be trained to identify people, or make decisions that impact people. We document these instructions and other terms-of-use in a datasheet [45], provided in Appendix G.
> **Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety?**
The scale of RedCaps means that we are unable to verify the contents of all images and
captions. However we have tried to minimize the possibility that RedCaps contains data that
might be offensive, insulting, threatening, or might cause anxiety via the following mitigations:
(a) We manually curate the set of subreddits from which to collect data; we only chose
subreddits that are not marked NSFW and which generally contain non-offensive content.
(b) Within our curated subreddits, we did not include any posts marked NSFW.
(c) We removed all instances whose captions contained any of the 400 potentially offensive
words or phrases. Refer Section 2.2 in the main paper.
(d) We remove all instances whose images were flagged NSFW by an off-the-shelf detector.
We manually checked 50K random images in RedCaps and found one image containing
nudity (exposed buttocks; no identifiable face). Refer Section 2.2 in the main paper
> **Does the dataset identify any subpopulations (e.g., by age, gender)?**
RedCaps does not explicitly identify any subpopulations. Since some images contain people
and captions are free-form natural language written by Reddit users, it is possible that some
captions may identify people appearing in individual images as part of a subpopulation.
> **Were any ethical review processes conducted (e.g., by an institutional review board)?**
We did not conduct a formal ethical review process via institutional review boards. However,
as described in Section 2.2 of the main paper and Q16 we employed several filtering mechanisms
to try and remove instances that could be problematic.
### Other Known Limitations
From the paper:
> **Are there any errors, sources of noise, or redundancies in the dataset?**
RedCaps is noisy by design since image-text pairs on the internet are noisy and unstructured.
Some instances may also have duplicate images and captions – Reddit users may have shared
the same image post in multiple subreddits. Such redundancies constitute a very small fraction
of the dataset, and should have almost no effect in training large-scale models.
> **Does the dataset contain data that might be considered confidential (e.g., data that is
protected by legal privilege or by doctor-patient confidentiality, data that includes the
content of individuals non-public communications)?**
No, the subreddits included in RedCaps do not cover topics that may be considered confidential. All posts were publicly shared on Reddit prior to inclusion in RedCaps.
## Additional Information
### Dataset Curators
From the paper:
> Four researchers at the University of Michigan (affiliated as of 2021) have created RedCaps:
Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson.
### Licensing Information
The image metadata is licensed under CC-BY 4.0 license. Additionally, uses of this dataset are subject to Reddit API terms (https://www.reddit.com/wiki/
api-terms) and users must comply with Reddit User Agreeement, Content Policy,
and Privacy Policy – all accessible at https://www.redditinc.com/policies.
From the paper:
> RedCaps should only be used for non-commercial research. RedCaps should not be used for any tasks that involve identifying features related to people (facial recognition, gender, age, ethnicity identification, etc.) or make decisions that impact people (mortgages, job applications, criminal sentences; or moderation decisions about user-uploaded data that could result in bans from a website). Any commercial and for-profit uses of RedCaps are restricted – it should not be used to train models that will be deployed in production systems as part of a product offered by businesses or government agencies.
### Citation Information
```bibtex
@misc{desai2021redcaps,
title={RedCaps: web-curated image-text data created by the people, for the people},
author={Karan Desai and Gaurav Kaul and Zubin Aysola and Justin Johnson},
year={2021},
eprint={2111.11431},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
### Contributions
Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
bertin-project/alpaca-spanish | 2023-03-24T11:38:19.000Z | [
"task_categories:text-generation",
"language:es",
"license:cc-by-4.0",
"instruction-finetuning",
"region:us"
] | bertin-project | null | null | null | 18 | 177 | ---
license: cc-by-4.0
language:
- es
tags:
- instruction-finetuning
pretty_name: BERTIN Alpaca Spanish
task_categories:
- text-generation
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 21439975
num_examples: 51942
download_size: 13178075
dataset_size: 21439975
---
# BERTIN Alpaca Spanish
This dataset is a translation to Spanish of [alpaca_data_cleaned.json](https://github.com/tloen/alpaca-lora/blob/main/alpaca_data_cleaned.json), a clean version of the [Alpaca dataset made at Stanford](https://huggingface.co/datasets/tatsu-lab/alpaca).
An [earlier version](https://huggingface.co/datasets/bertin-project/alpaca-spanish/blob/main/nllb/spa_train.json.gz) used [Facebook's NLLB 1.3B model](https://huggingface.co/facebook/nllb-200-1.3B), but the current version uses OpenAI's `gpt-3.5-turbo`, hence this dataset cannot be used to create models that compete in any way against OpenAI. |
thefcraft/civitai-stable-diffusion-337k | 2023-09-26T07:10:40.000Z | [
"annotations_creators:no-annotation",
"language_creators:thefcraft",
"size_categories:1M<n<10M",
"source_datasets:civitai",
"language:en",
"region:us"
] | thefcraft | null | null | null | 8 | 177 | ---
annotations_creators:
- no-annotation
language_creators:
- thefcraft
language:
- en
pretty_name: civitai-stable-diffusion-337k
size_categories:
- 1M<n<10M
source_datasets:
- civitai
---
### How to Use
```
from datasets import load_dataset
dataset = load_dataset("thefcraft/civitai-stable-diffusion-337k")
print(dataset['train'][0])
```
### download images
download zip files from images dir
https://huggingface.co/datasets/thefcraft/civitai-stable-diffusion-337k/tree/main/images
it contains some images with id
```
from zipfile import ZipFile
with ZipFile("filename.zip", 'r') as zObject: zObject.extractall()
```
### Dataset Summary
GitHub URL:- https://github.com/thefcraft/civitai-stable-diffusion-337k
dataset:- civitai-stable-diffusion-337k this dataset contains 337k civitai images url with prompts etc. i use civitai api to get all prompts.
project:- https://github.com/thefcraft/nsfw-prompt-detection-sd I train a model on this dataset
DATA STRUCTURE for othertype/civitai.json:-
```{
'items':[
{'id': 100657,
'url': 'https://imagecache.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/2338276a-87f7-4a1e-f92a-776a18ee4200/width=768/2338276a-87f7-4a1e-f92a-776a18ee4200.jpeg',
'hash': 'U5Exz_00.8D$t89Z%M0100~VD*RktQxaIU~p',
'width': 768,
'height': 1368,
'nsfw': True,
'createdAt': '2023-02-14T10:05:11.498Z',
'postId': 60841,
'stats': {'cryCount': 0,
'laughCount': 0,
'likeCount': 26,
'dislikeCount': 0,
'heartCount': 50,
'commentCount': 4},
'meta': {'ENSD': '31337',
'Size': '512x912',
'seed': 3994946333,
'Model': 'AbyssOrangeMix2_sfw',
'steps': 20,
'prompt': '<lora:hiqcg_body-epoch-000004:0.5>, <lora:hiqcg_face-epoch-000004:0.4>, hiqcgbody, hiqcgface, 1girl, full body, standing, \ndetailed skin texture, detailed cloth texture, beautiful detailed face,\nmasterpiece, best quality, ultra detailed, 8k, intricate details,',
'sampler': 'DPM++ 2M Karras',
'cfgScale': 7,
'Clip skip': '2',
'resources': [{'hash': '038ba203d8',
'name': 'AbyssOrangeMix2_sfw',
'type': 'model'}],
'Model hash': '038ba203d8',
'Hires upscale': '1.5',
'Hires upscaler': 'Latent',
'negativePrompt': 'EasyNegative, extra fingers,fewer fingers, multiple girls, multiple views,',
'Denoising strength': '0.6'},
'username': 'NeoClassicalRibbon'},
{..},
..],
'metadata':{'totalItems': 327145}
}
```
|
ChilleD/MultiArith | 2023-05-02T01:44:21.000Z | [
"region:us"
] | ChilleD | null | null | null | 1 | 177 | Entry not found |
jinho8345/funsd | 2023-07-29T09:06:10.000Z | [
"region:us"
] | jinho8345 | null | null | null | 0 | 177 | ---
dataset_info:
features:
- name: img
dtype: image
- name: filename
dtype: string
- name: boxes
sequence:
sequence: int64
- name: labels
sequence: string
- name: words
list:
list:
- name: box
sequence: int64
- name: text
dtype: string
- name: linkings
sequence:
sequence:
sequence: int64
- name: ids
sequence: int64
splits:
- name: train
num_bytes: 13690247.0
num_examples: 149
- name: test
num_bytes: 4885049.0
num_examples: 50
download_size: 16731921
dataset_size: 18575296.0
---
# Dataset Card for "funsd"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
break_data | 2023-04-05T09:42:04.000Z | [
"task_categories:text2text-generation",
"task_ids:open-domain-abstractive-qa",
"annotations_creators:crowdsourced",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:en",
"license:unknown",
"region:us"
] | null | Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations
(QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases.
This repository contains the Break dataset along with information on the exact data format. | @article{Wolfson2020Break,
title={Break It Down: A Question Understanding Benchmark},
author={Wolfson, Tomer and Geva, Mor and Gupta, Ankit and Gardner, Matt and Goldberg, Yoav and Deutch, Daniel and Berant, Jonathan},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
} | null | 0 | 176 | ---
annotations_creators:
- crowdsourced
language_creators:
- crowdsourced
language:
- en
license:
- unknown
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- open-domain-abstractive-qa
paperswithcode_id: break
pretty_name: BREAK
dataset_info:
- config_name: QDMR-high-level
features:
- name: question_id
dtype: string
- name: question_text
dtype: string
- name: decomposition
dtype: string
- name: operators
dtype: string
- name: split
dtype: string
splits:
- name: test
num_bytes: 482339
num_examples: 3195
- name: train
num_bytes: 5148086
num_examples: 17503
- name: validation
num_bytes: 914780
num_examples: 3130
download_size: 15971078
dataset_size: 6545205
- config_name: QDMR-high-level-lexicon
features:
- name: source
dtype: string
- name: allowed_tokens
dtype: string
splits:
- name: test
num_bytes: 4240755
num_examples: 3195
- name: train
num_bytes: 23234518
num_examples: 17503
- name: validation
num_bytes: 4158679
num_examples: 3130
download_size: 15971078
dataset_size: 31633952
- config_name: QDMR
features:
- name: question_id
dtype: string
- name: question_text
dtype: string
- name: decomposition
dtype: string
- name: operators
dtype: string
- name: split
dtype: string
splits:
- name: test
num_bytes: 900632
num_examples: 8069
- name: train
num_bytes: 12790466
num_examples: 44321
- name: validation
num_bytes: 2237472
num_examples: 7760
download_size: 15971078
dataset_size: 15928570
- config_name: QDMR-lexicon
features:
- name: source
dtype: string
- name: allowed_tokens
dtype: string
splits:
- name: test
num_bytes: 10331822
num_examples: 8069
- name: train
num_bytes: 56913064
num_examples: 44321
- name: validation
num_bytes: 9936933
num_examples: 7760
download_size: 15971078
dataset_size: 77181819
- config_name: logical-forms
features:
- name: question_id
dtype: string
- name: question_text
dtype: string
- name: decomposition
dtype: string
- name: operators
dtype: string
- name: split
dtype: string
- name: program
dtype: string
splits:
- name: test
num_bytes: 927038
num_examples: 8006
- name: train
num_bytes: 19821676
num_examples: 44098
- name: validation
num_bytes: 3504893
num_examples: 7719
download_size: 15971078
dataset_size: 24253607
---
# Dataset Card for "break_data"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [https://github.com/allenai/Break](https://github.com/allenai/Break)
- **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Paper:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 79.86 MB
- **Size of the generated dataset:** 155.55 MB
- **Total amount of disk used:** 235.39 MB
### Dataset Summary
Break is a human annotated dataset of natural language questions and their Question Decomposition Meaning Representations
(QDMRs). Break consists of 83,978 examples sampled from 10 question answering datasets over text, images and databases.
This repository contains the Break dataset along with information on the exact data format.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### QDMR
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 15.93 MB
- **Total amount of disk used:** 31.90 MB
An example of 'validation' looks as follows.
```
{
"decomposition": "return flights ;return #1 from denver ;return #2 to philadelphia ;return #3 if available",
"operators": "['select', 'filter', 'filter', 'filter']",
"question_id": "ATIS_dev_0",
"question_text": "what flights are available tomorrow from denver to philadelphia ",
"split": "dev"
}
```
#### QDMR-high-level
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 6.54 MB
- **Total amount of disk used:** 22.51 MB
An example of 'train' looks as follows.
```
{
"decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4",
"operators": "['select', 'filter', 'filter', 'filter', 'project']",
"question_id": "ATIS_dev_102",
"question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ",
"split": "dev"
}
```
#### QDMR-high-level-lexicon
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 31.64 MB
- **Total amount of disk used:** 47.61 MB
An example of 'train' looks as follows.
```
This example was too long and was cropped:
{
"allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'he', 'distinct', 'House', 'two', 'at least', 'or ', 'date', 'o...",
"source": "What office, also held by a member of the Maine House of Representatives, did James K. Polk hold before he was president?"
}
```
#### QDMR-lexicon
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 77.19 MB
- **Total amount of disk used:** 93.16 MB
An example of 'validation' looks as follows.
```
This example was too long and was cropped:
{
"allowed_tokens": "\"['higher than', 'same as', 'what ', 'and ', 'than ', 'at most', 'distinct', 'two', 'at least', 'or ', 'date', 'on ', '@@14@@', ...",
"source": "what flights are available tomorrow from denver to philadelphia "
}
```
#### logical-forms
- **Size of downloaded dataset files:** 15.97 MB
- **Size of the generated dataset:** 24.25 MB
- **Total amount of disk used:** 40.22 MB
An example of 'train' looks as follows.
```
{
"decomposition": "return ground transportation ;return #1 which is available ;return #2 from the pittsburgh airport ;return #3 to downtown ;return the cost of #4",
"operators": "['select', 'filter', 'filter', 'filter', 'project']",
"program": "some program",
"question_id": "ATIS_dev_102",
"question_text": "what ground transportation is available from the pittsburgh airport to downtown and how much does it cost ",
"split": "dev"
}
```
### Data Fields
The data fields are the same among all splits.
#### QDMR
- `question_id`: a `string` feature.
- `question_text`: a `string` feature.
- `decomposition`: a `string` feature.
- `operators`: a `string` feature.
- `split`: a `string` feature.
#### QDMR-high-level
- `question_id`: a `string` feature.
- `question_text`: a `string` feature.
- `decomposition`: a `string` feature.
- `operators`: a `string` feature.
- `split`: a `string` feature.
#### QDMR-high-level-lexicon
- `source`: a `string` feature.
- `allowed_tokens`: a `string` feature.
#### QDMR-lexicon
- `source`: a `string` feature.
- `allowed_tokens`: a `string` feature.
#### logical-forms
- `question_id`: a `string` feature.
- `question_text`: a `string` feature.
- `decomposition`: a `string` feature.
- `operators`: a `string` feature.
- `split`: a `string` feature.
- `program`: a `string` feature.
### Data Splits
| name |train|validation|test|
|-----------------------|----:|---------:|---:|
|QDMR |44321| 7760|8069|
|QDMR-high-level |17503| 3130|3195|
|QDMR-high-level-lexicon|17503| 3130|3195|
|QDMR-lexicon |44321| 7760|8069|
|logical-forms |44098| 7719|8006|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{Wolfson2020Break,
title={Break It Down: A Question Understanding Benchmark},
author={Wolfson, Tomer and Geva, Mor and Gupta, Ankit and Gardner, Matt and Goldberg, Yoav and Deutch, Daniel and Berant, Jonathan},
journal={Transactions of the Association for Computational Linguistics},
year={2020},
}
```
### Contributions
Thanks to [@patrickvonplaten](https://github.com/patrickvonplaten), [@lewtun](https://github.com/lewtun), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
kinnews_kirnews | 2023-06-01T14:59:50.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:topic-classification",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:rn",
"language:rw",
"license:mit",
"arxiv:2010.12174",
"region:us"
] | null | Kinyarwanda and Kirundi news classification datasets | @article{niyongabo2020kinnews,
title={KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi},
author={Niyongabo, Rubungo Andre and Qu, Hong and Kreutzer, Julia and Huang, Li},
journal={arXiv preprint arXiv:2010.12174},
year={2020}
} | null | 1 | 176 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- rn
- rw
license:
- mit
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
- 1K<n<10K
source_datasets:
- original
task_categories:
- text-classification
task_ids:
- multi-class-classification
- topic-classification
paperswithcode_id: kinnews-and-kirnews
pretty_name: KinnewsKirnews
dataset_info:
- config_name: kinnews_raw
features:
- name: label
dtype:
class_label:
names:
'0': politics
'1': sport
'2': economy
'3': health
'4': entertainment
'5': history
'6': technology
'7': tourism
'8': culture
'9': fashion
'10': religion
'11': environment
'12': education
'13': relationship
- name: kin_label
dtype: string
- name: en_label
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 38316546
num_examples: 17014
- name: test
num_bytes: 11971938
num_examples: 4254
download_size: 27377755
dataset_size: 50288484
- config_name: kinnews_cleaned
features:
- name: label
dtype:
class_label:
names:
'0': politics
'1': sport
'2': economy
'3': health
'4': entertainment
'5': history
'6': technology
'7': tourism
'8': culture
'9': fashion
'10': religion
'11': environment
'12': education
'13': relationship
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 32780382
num_examples: 17014
- name: test
num_bytes: 8217453
num_examples: 4254
download_size: 27377755
dataset_size: 40997835
- config_name: kirnews_raw
features:
- name: label
dtype:
class_label:
names:
'0': politics
'1': sport
'2': economy
'3': health
'4': entertainment
'5': history
'6': technology
'7': tourism
'8': culture
'9': fashion
'10': religion
'11': environment
'12': education
'13': relationship
- name: kir_label
dtype: string
- name: en_label
dtype: string
- name: url
dtype: string
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 7343223
num_examples: 3689
- name: test
num_bytes: 2499189
num_examples: 923
download_size: 5186111
dataset_size: 9842412
- config_name: kirnews_cleaned
features:
- name: label
dtype:
class_label:
names:
'0': politics
'1': sport
'2': economy
'3': health
'4': entertainment
'5': history
'6': technology
'7': tourism
'8': culture
'9': fashion
'10': religion
'11': environment
'12': education
'13': relationship
- name: title
dtype: string
- name: content
dtype: string
splits:
- name: train
num_bytes: 6629767
num_examples: 3689
- name: test
num_bytes: 1570745
num_examples: 923
download_size: 5186111
dataset_size: 8200512
config_names:
- kinnews_cleaned
- kinnews_raw
- kirnews_cleaned
- kirnews_raw
---
# Dataset Card for kinnews_kirnews
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** [More Information Needed]
- **Repository:** https://github.com/Andrews2017/KINNEWS-and-KIRNEWS-Corpus
- **Paper:** [KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi](https://arxiv.org/abs/2010.12174)
- **Leaderboard:** NA
- **Point of Contact:** [Rubungo Andre Niyongabo1](mailto:niyongabor.andre@std.uestc.edu.cn)
### Dataset Summary
Kinyarwanda and Kirundi news classification datasets (KINNEWS and KIRNEWS,respectively), which were both collected from Rwanda and Burundi news websites and newspapers, for low-resource monolingual and cross-lingual multiclass classification tasks.
### Supported Tasks and Leaderboards
This dataset can be used for text classification of news articles in Kinyarwadi and Kirundi languages. Each news article can be classified into one of the 14 possible classes. The classes are:
- politics
- sport
- economy
- health
- entertainment
- history
- technology
- culture
- religion
- environment
- education
- relationship
### Languages
Kinyarwanda and Kirundi
## Dataset Structure
### Data Instances
Here is an example from the dataset:
| Field | Value |
| ----- | ----------- |
| label | 1 |
| kin_label/kir_label | 'inkino' |
| url | 'https://nawe.bi/Primus-Ligue-Imirwi-igiye-guhura-gute-ku-ndwi-ya-6-y-ihiganwa.html' |
| title | 'Primus Ligue\xa0: Imirwi igiye guhura gute ku ndwi ya 6 y’ihiganwa\xa0?'|
| content | ' Inkino zitegekanijwe kuruno wa gatandatu igenekerezo rya 14 Nyakanga umwaka wa 2019...'|
| en_label| 'sport'|
### Data Fields
The raw version of the data for Kinyarwanda language consists of these fields
- label: The category of the news article
- kin_label/kir_label: The associated label in Kinyarwanda/Kirundi language
- en_label: The associated label in English
- url: The URL of the news article
- title: The title of the news article
- content: The content of the news article
The cleaned version contains only the `label`, `title` and the `content` fields
### Data Splits
Lang| Train | Test |
|---| ----- | ---- |
|Kinyarwandai Raw|17014|4254|
|Kinyarwandai Clean|17014|4254|
|Kirundi Raw|3689|923|
|Kirundi Clean|3689|923|
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
```
@article{niyongabo2020kinnews,
title={KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi},
author={Niyongabo, Rubungo Andre and Qu, Hong and Kreutzer, Julia and Huang, Li},
journal={arXiv preprint arXiv:2010.12174},
year={2020}
}
```
### Contributions
Thanks to [@saradhix](https://github.com/saradhix) for adding this dataset. |
search_qa | 2023-06-16T09:03:21.000Z | [
"task_categories:question-answering",
"task_ids:extractive-qa",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:original",
"language:en",
"license:unknown",
"arxiv:1704.05179",
"region:us"
] | null | We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind
CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article
and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google.
Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context
tuple of the SearchQA comes with additional meta-data such as the snippet's URL, which we believe will be valuable resources for future research. We conduct human evaluation
as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human
and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering. | null | null | 10 | 176 | ---
annotations_creators:
- found
language:
- en
language_creators:
- found
license:
- unknown
multilinguality:
- monolingual
pretty_name: SearchQA
size_categories:
- 100K<n<1M
source_datasets:
- original
task_categories:
- question-answering
task_ids:
- extractive-qa
paperswithcode_id: searchqa
dataset_info:
- config_name: raw_jeopardy
features:
- name: category
dtype: string
- name: air_date
dtype: string
- name: question
dtype: string
- name: value
dtype: string
- name: answer
dtype: string
- name: round
dtype: string
- name: show_number
dtype: int32
- name: search_results
sequence:
- name: urls
dtype: string
- name: snippets
dtype: string
- name: titles
dtype: string
- name: related_links
dtype: string
splits:
- name: train
num_bytes: 7770972348
num_examples: 216757
download_size: 3314386157
dataset_size: 7770972348
- config_name: train_test_val
features:
- name: category
dtype: string
- name: air_date
dtype: string
- name: question
dtype: string
- name: value
dtype: string
- name: answer
dtype: string
- name: round
dtype: string
- name: show_number
dtype: int32
- name: search_results
sequence:
- name: urls
dtype: string
- name: snippets
dtype: string
- name: titles
dtype: string
- name: related_links
dtype: string
splits:
- name: train
num_bytes: 5303005740
num_examples: 151295
- name: test
num_bytes: 1466749978
num_examples: 43228
- name: validation
num_bytes: 740962715
num_examples: 21613
download_size: 3148550732
dataset_size: 7510718433
---
# Dataset Card for "search_qa"
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Repository:** https://github.com/nyu-dl/dl4ir-searchQA
- **Paper:** [SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine](https://arxiv.org/abs/1704.05179)
- **Point of Contact:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
- **Size of downloaded dataset files:** 6.46 GB
- **Size of the generated dataset:** 15.28 GB
- **Total amount of disk used:** 21.74 GB
### Dataset Summary
We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering. Unlike recently released datasets, such as DeepMind
CNN/DailyMail and SQuAD, the proposed SearchQA was constructed to reflect a full pipeline of general question-answering. That is, we start not from an existing article
and generate a question-answer pair, but start from an existing question-answer pair, crawled from J! Archive, and augment it with text snippets retrieved by Google.
Following this approach, we built SearchQA, which consists of more than 140k question-answer pairs with each pair having 49.6 snippets on average. Each question-answer-context
tuple of the SearchQA comes with additional meta-data such as the snippet's URL, which we believe will be valuable resources for future research. We conduct human evaluation
as well as test two baseline methods, one simple word selection and the other deep learning based, on the SearchQA. We show that there is a meaningful gap between the human
and machine performances. This suggests that the proposed dataset could well serve as a benchmark for question-answering.
### Supported Tasks and Leaderboards
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Languages
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Dataset Structure
### Data Instances
#### raw_jeopardy
- **Size of downloaded dataset files:** 3.31 GB
- **Size of the generated dataset:** 7.77 GB
- **Total amount of disk used:** 11.09 GB
An example of 'train' looks as follows.
```
```
#### train_test_val
- **Size of downloaded dataset files:** 3.15 GB
- **Size of the generated dataset:** 7.51 GB
- **Total amount of disk used:** 10.66 GB
An example of 'validation' looks as follows.
```
```
### Data Fields
The data fields are the same among all splits.
#### raw_jeopardy
- `category`: a `string` feature.
- `air_date`: a `string` feature.
- `question`: a `string` feature.
- `value`: a `string` feature.
- `answer`: a `string` feature.
- `round`: a `string` feature.
- `show_number`: a `int32` feature.
- `search_results`: a dictionary feature containing:
- `urls`: a `string` feature.
- `snippets`: a `string` feature.
- `titles`: a `string` feature.
- `related_links`: a `string` feature.
#### train_test_val
- `category`: a `string` feature.
- `air_date`: a `string` feature.
- `question`: a `string` feature.
- `value`: a `string` feature.
- `answer`: a `string` feature.
- `round`: a `string` feature.
- `show_number`: a `int32` feature.
- `search_results`: a dictionary feature containing:
- `urls`: a `string` feature.
- `snippets`: a `string` feature.
- `titles`: a `string` feature.
- `related_links`: a `string` feature.
### Data Splits
#### raw_jeopardy
| |train |
|------------|-----:|
|raw_jeopardy|216757|
#### train_test_val
| |train |validation|test |
|--------------|-----:|---------:|----:|
|train_test_val|151295| 21613|43228|
## Dataset Creation
### Curation Rationale
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the source language producers?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Annotations
#### Annotation process
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
#### Who are the annotators?
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Personal and Sensitive Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Discussion of Biases
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Other Known Limitations
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
## Additional Information
### Dataset Curators
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Licensing Information
[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
### Citation Information
```
@article{DBLP:journals/corr/DunnSHGCC17,
author = {Matthew Dunn and
Levent Sagun and
Mike Higgins and
V. Ugur G{"{u}}ney and
Volkan Cirik and
Kyunghyun Cho},
title = {SearchQA: {A} New Q{\&}A Dataset Augmented with Context from a
Search Engine},
journal = {CoRR},
volume = {abs/1704.05179},
year = {2017},
url = {http://arxiv.org/abs/1704.05179},
archivePrefix = {arXiv},
eprint = {1704.05179},
timestamp = {Mon, 13 Aug 2018 16:47:09 +0200},
biburl = {https://dblp.org/rec/journals/corr/DunnSHGCC17.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
### Contributions
Thanks to [@lewtun](https://github.com/lewtun), [@mariamabarham](https://github.com/mariamabarham), [@lhoestq](https://github.com/lhoestq), [@thomwolf](https://github.com/thomwolf) for adding this dataset. |
nielsr/CelebA-faces | 2022-03-21T14:48:37.000Z | [
"region:us"
] | nielsr | null | null | null | 3 | 176 | Entry not found |
EleutherAI/pythia-memorized-evals | 2023-03-14T15:12:36.000Z | [
"region:us"
] | EleutherAI | null | null | null | 1 | 176 | ---
dataset_info:
features:
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dtype: int64
- name: tokens
sequence: int64
- name: __index_level_0__
dtype: int64
splits:
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- name: deduped.1b
num_bytes: 549484180
num_examples: 1032865
download_size: 2931941971
dataset_size: 10492937112
---
# Dataset Card for "pythia-memorized-evals"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
vjain/Therapy | 2023-05-16T22:31:22.000Z | [
"region:us"
] | vjain | null | null | null | 1 | 176 | Entry not found |
result-kand2-sdxl-wuerst-karlo/040dec0a | 2023-10-03T08:51:41.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 176 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
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num_bytes: 160
num_examples: 10
download_size: 1292
dataset_size: 160
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "040dec0a"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
result-kand2-sdxl-wuerst-karlo/f4d8fc49 | 2023-10-03T08:54:45.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 176 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 159
num_examples: 10
download_size: 1306
dataset_size: 159
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "f4d8fc49"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
atasoglu/databricks-dolly-15k-tr | 2023-05-01T10:30:39.000Z | [
"task_categories:question-answering",
"size_categories:10K<n<100K",
"language:tr",
"license:cc-by-sa-3.0",
"region:us"
] | atasoglu | null | null | null | 6 | 175 | ---
license: cc-by-sa-3.0
task_categories:
- question-answering
language:
- tr
pretty_name: databricks-dolly-15k-tr
size_categories:
- 10K<n<100K
---
This dataset is machine-translated version of [databricks-dolly-15k.jsonl](https://github.com/databrickslabs/dolly/tree/master/data) into Turkish.
Used `googletrans==3.1.0a0` to translation. |
Mike0307/MNIST-M | 2023-07-04T15:10:25.000Z | [
"license:mit",
"region:us"
] | Mike0307 | null | null | null | 0 | 175 | ---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: label
dtype:
class_label:
names:
'0': '0'
'1': '1'
'2': '2'
'3': '3'
'4': '4'
'5': '5'
'6': '6'
'7': '7'
'8': '8'
'9': '9'
splits:
- name: train
num_bytes: 119131988.027
num_examples: 59001
- name: test
num_bytes: 18049625.166
num_examples: 9001
download_size: 143468539
dataset_size: 137181613.193
---
|
FreedomIntelligence/CMB | 2023-08-19T09:45:53.000Z | [
"task_categories:question-answering",
"task_categories:text-generation",
"size_categories:100K<n<1M",
"language:zh",
"license:apache-2.0",
"medical",
"biology",
"chemistry",
"region:us"
] | FreedomIntelligence |
Chinese Medical Benchmark | coming soon~ | null | 5 | 175 | ---
license: apache-2.0
task_categories:
- question-answering
- text-generation
language:
- zh
tags:
- medical
- biology
- chemistry
size_categories:
- 100K<n<1M
---
# CMB: A Comprehensive Medical Benchmark in Chinese
<p align="center">
🌐 <a href="https://cmedbenchmark.llmzoo.com/#home" target="_blank">Website</a> • 🤗 <a href="https://huggingface.co/datasets/FreedomIntelligence/CMB" target="_blank">HuggingFace</a>
## 🌈 Update
* **[2023.08.01]** 🎉🎉🎉 CMB is published！🎉🎉🎉
## 🌐 Download Data
- (Recommended) Download the [zip file](https://github.com/FreedomIntelligence/CMB/tree/main/data) and unzip:
```bash
git clone "https://github.com/FreedomIntelligence/CMB.git" && cd CMB && unzip "./data/CMB.zip" -d "./data/" && rm "./data/CMB.zip"
```
- Or load our data as follows:
```python
from datasets import load_dataset
# CMB-Exam datasets （multiple-choice and multiple-answer questions）
exam_datasets = load_dataset('FreedomIntelligence/CMB','exam')
# CMB-Clin datasets
clin_datasets = load_dataset('FreedomIntelligence/CMB','clin')
```
## 🥇 Leaderboard
Please Check [Leaderboard](https://cmedbenchmark.llmzoo.com/static/leaderboard.html).
## 🥸 Dataset intro
### Components
- CMB-Exam: Comprehensive multi-level assessment for medical knowledge
- Structure: 6 major categories and 28 subcategories, [View Catalog](catalog.md)
- CMB-test: 400 questions per subcategories, 11200 questions in total
- CMB-val: 280 questions with solutions and explanations; used as source for CoT and few-shot
- CMB-train: 269359 questions for medical knowledge injection
- CMB-Clin: 74 cases of complex medical inquires
### CMB-Exam Item
```json
{
"exam_type": "医师考试",
"exam_class": "执业医师",
"exam_subject": "口腔执业医师",
"question": "患者，男性，11岁。近2个月来时有低热（37～38℃），全身无明显症状。查体无明显阳性体征。X线检查发现右肺中部有一直径约0.8cm类圆形病灶，边缘稍模糊，肺门淋巴结肿大。此男孩可能患",
"answer": "D",
"question_type": "单项选择题",
"option": {
"A": "小叶型肺炎",
"B": "浸润性肺结核",
"C": "继发性肺结核",
"D": "原发性肺结核",
"E": "粟粒型肺结核"
}
},
```
- exam_type: major category
- exam_class: sub-category
- exam_subject: Specific departments or subdivisions of disciplines
- question_type: *multiple-choice (单项选择题)* or *multiple-answer (多项选择题)*
### CMB-Clin Item
```json
{
"id": 0,
"title": "案例分析-腹外疝",
"description": "现病史\n（1）病史摘要\n 病人，男，49岁，3小时前解大便后出现右下腹疼痛，右下腹可触及一包块，既往体健。\n（2）主诉\n 右下腹痛并自扪及包块3小时。\n\n体格检查\n体温： T 37.8℃，P 101次／分，呼吸22次/分，BP 100/60mmHg，腹软，未见胃肠型蠕动波，肝脾肋下未及，于右侧腹股沟区可扪及一圆形肿块，约4cm×4cm大小，有压痛、界欠清，且肿块位于腹股沟韧带上内方。\n\n辅助检查\n（1）实验室检查\n 血常规：WBC 5.0×109／L，N 78％。\n 尿常规正常。\n（2）多普勒超声检查\n 沿腹股沟纵切可见一多层分布的混合回声区，宽窄不等，远端膨大，边界整齐，长约4～5cm。\n（3）腹部X线检查\n 可见阶梯状液气平。",
"QA_pairs": [
{
"question": "简述该病人的诊断及诊断依据。",
"solution": "诊断：嵌顿性腹股沟斜疝合并肠梗阻。\n诊断依据：\n①右下腹痛并自扪及包块3小时；\n②有腹胀、呕吐，类似肠梗阻表现；腹部平片可见阶梯状液平，考虑肠梗阻可能；腹部B超考虑，\n腹部包块内可能为肠管可能；\n③有轻度毒性反应或是中毒反应，如 T 37.8℃，P 101次／分，白细胞中性分类78％；\n④腹股沟区包块位于腹股沟韧带上内方。"
},
{
"question": "简述该病人的鉴别诊断。",
"solution": "（1）睾丸鞘膜积液：鞘膜积液所呈现的肿块完全局限在阴囊内，其上界可以清楚地摸到；用透光试验检查肿块，鞘膜积液多为透光（阳性），而疝块则不能透光。\n（2）交通性鞘膜积液：肿块的外形与睾丸鞘膜积液相似。于每日起床后或站立活动时肿块缓慢地出现并增大。平卧或睡觉后肿块逐渐缩小，挤压肿块，其体积也可逐渐缩小。透光试验为阳性。\n（3）精索鞘膜积液：肿块较小，在腹股沟管内，牵拉同侧睾丸可见肿块移动。\n（4）隐睾：腹股沟管内下降不全的睾丸可被误诊为斜疝或精索鞘膜积液。隐睾肿块较小，挤压时可出现特有的胀痛感觉。如患侧阴囊内睾丸缺如，则诊断更为明确。\n（5）急性肠梗阻：肠管被嵌顿的疝可伴发急性肠梗阻，但不应仅满足于肠梗阻的诊断而忽略疝的存在；尤其是病人比较肥胖或疝块较小时，更易发生这类问题而导致治疗上的错误。\n（6）此外，腹股沟区肿块还应与以下疾病鉴别:肿大的淋巴结、动（静）脉瘤、软组织肿瘤、脓肿、\n圆韧带囊肿、子宫内膜异位症等。"
},
{
"question": "简述该病人的治疗原则。",
"solution": "嵌顿性疝原则上需要紧急手术治疗，以防止疝内容物坏死并解除伴发的肠梗阻。术前应做好必要的准备，如有脱水和电解质紊乱，应迅速补液加以纠正。手术的关键在于正确判断疝内容物的活力，然后根据病情确定处理方法。在扩张或切开疝环、解除疝环压迫的前提下，凡肠管呈紫黑色，失去光泽和弹性，刺激后无蠕动和相应肠系膜内无动脉搏动者，即可判定为肠坏死。如肠管尚未坏死，则可将其送回腹腔，按一般易复性疝处理，即行疝囊高位结扎+疝修补术。如肠管确已坏死或一时不能肯定肠管是否已失去活力时，则应在病人全身情况允许的前提下，切除该段肠管并进行一期吻合。凡施行肠切除吻合术的病人，因手术区污染，在高位结扎疝囊后，一般不宜作疝修补术，以免因感染而致修补失败。"
}
]
},
```
- title: name of disease
- description: information of patient
- QA_pairs: a series of questions and their solutions based on the description
## ℹ️ How to evaluate and submit refer to [link](https://github.com/FreedomIntelligence/CMB)
## 😘 Citation
Please use the following citation if you intend to use our dataset for training or evaluation:
```
@misc{cmedbenchmark,
title={CMB: Chinese Medical Benchmark},
author={Xidong Wang*, Guiming Hardy Chen*, Dingjie Song*, Zhiyi Zhang*, Qingying Xiao, Xiangbo Wu, Feng Jiang, Jianquan Li, Benyou Wang},
note={Xidong Wang, Guiming Hardy Chen, Dingjie Song, and Zhiyi Zhang contributed equally to this github repo.},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/FreedomIntelligence/CMB}},
}
```
## Acknowledgement
- We thank [Shenzhen Research Institute of Big Data](http://www.sribd.cn/) for their enormous support for this project.
- We thank the following doctors for participating in the human evaluation of CMB-Clin:
- 林士军 (香港中文大学（深圳）附属第二医院)
- 常河
- 许晓爽
|
kandriiashevskyi/wix_looker_ai | 2023-10-10T09:12:52.000Z | [
"region:us"
] | kandriiashevskyi | null | null | null | 0 | 175 | Entry not found |
emozilla/pg19 | 2023-10-09T15:06:39.000Z | [
"region:us"
] | emozilla | null | null | null | 3 | 175 | ---
dataset_info:
features:
- name: short_book_title
dtype: string
- name: publication_date
dtype: int32
- name: url
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 11453688452
num_examples: 28602
- name: validation
num_bytes: 17402295
num_examples: 50
- name: test
num_bytes: 40482852
num_examples: 100
download_size: 2257437892
dataset_size: 11511573599
---
# Dataset Card for "pg19"
Paraquet version of [pg19](https://huggingface.co/datasets/pg19)
Statistics (in # of characters): `total_len: 11425076324, average_len: 399450.2595622684` |
result-kand2-sdxl-wuerst-karlo/6a3f723d | 2023-10-03T08:48:05.000Z | [
"region:us"
] | result-kand2-sdxl-wuerst-karlo | null | null | null | 0 | 175 | ---
dataset_info:
features:
- name: result
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 162
num_examples: 10
download_size: 1317
dataset_size: 162
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "6a3f723d"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
iohadrubin/smcalflow | 2022-01-01T20:57:52.000Z | [
"region:us"
] | iohadrubin | null | 2 | 174 | Entry not found | ||
ranpox/xfund | 2021-09-08T11:15:02.000Z | [
"region:us"
] | ranpox | null | null | null | 3 | 174 | Entry not found |
JeremyAlain/SLF5K | 2023-01-24T14:21:35.000Z | [
"task_categories:summarization",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"source_datasets:original",
"language:en",
"license:apache-2.0",
"feedback",
"human feedback",
"language feedback",
"binary feedback",
"reward",
"reward model",
"gpt3",
"gpt-3",
"instructgpt",
"alignment",
"ai alignment",
"scale",
"imitation learning from language feedback",
"ilf",
"arxiv:2009.01325",
"region:us"
] | JeremyAlain | The Summarization with Language Feedback (SLF5K) dataset is an English-language dataset containing 5K unique samples that can be used for the task of abstraction summarization. Each sample consists of a Reddit title and post, a model-generated (FeedME) summary, and human-written language feedback on that summary. Additionally, each sample has a high-quality, human-written (gold) summary that should be ideal for the Reddit post. Lastly, each sample has two additional model-generated summaries with binary human preference labels, on which summary is preferred by a human. The dataset can be used to train language models with language feedback on abstractive summarization. It can also be used to train a reward model on binary preferences. | @article{
} | null | 4 | 174 | ---
annotations_creators:
- expert-generated
language:
- en
language_creators:
- found
license: apache-2.0
multilinguality:
- monolingual
pretty_name: SLF5K
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- feedback
- human feedback
- language feedback
- binary feedback
- reward
- reward model
- gpt3
- gpt-3
- instructgpt
- alignment
- ai alignment
- scale
- imitation learning from language feedback
- ilf
task_categories:
- summarization
task_ids: []
---
# Dataset Card for SLF5K
## Dataset Description
- **Repository: https://github.com/JeremyAlain/imitation_learning_from_language_feedback**
- **Paper: Training Language Models with Language Feedback at Scale**
- **Point of Contact: jeremy.scheurer@nyu.edu and ethan@anthropic.com**
### Dataset Summary
The Summarization with Language Feedback (SLF5K) dataset is an English-language dataset containing 5K unique samples that can be used
for the task of abstraction summarization. Each sample consists
of a Reddit title and post, a model-generated ([FeedME](https://beta.openai.com/docs/model-index-for-researchers)) summary, and human-written language feedback on that summary.
Additionally, each sample has a high-quality, human-written (gold) summary that should be ideal for the Reddit post.
Lastly, each sample has two additional model-generated summaries with binary human preference labels, on which summary is preferred by a human.
The dataset can be used to train language models with language feedback on abstractive summarization. It can also be
used to train a reward model on binary preferences.
The Reddit posts were taken from the datasets provided by [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf), who used the initial Reddit post dataset
[TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf).
### Supported Tasks and Leaderboards
The dataset can be used to train a model for abstractive and extractive summarization. It can either be trained directly on
human-written summaries, or leverage language feedback or binary human preferences.
The model performance is evaluated in a human evaluation, where annotators rate the quality of the generated summaries.
Previous work has used [ROUGE](https://huggingface.co/spaces/evaluate-metric/rouge) scores, but in [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf) they
show that ROUGE is not an ideal metric.
### Languages
English
## Dataset Structure
### Data Instances
Each instance is a line in the dataset file (which is saved as .jsonl). Each instance contains various fields, where the most important are
Here is an example instance:
```
{"id":"t3_3w7gyp",
"subreddit":"dogs",
"title":"Puppy playing at park - other owner aggressive towards him [help]",
"post":"Hi all, looking for some advice. I have a 6m old kelpie, buzz, who goes with me daily to a dog park, [...]",
"tldr_human_reference_summary":"other owner at park harsh with my dog for playing to rough with his. Have tried talking to him about it, hasn't helped.",
"summary_prompt":"Write an excellent summary of the given text.\n\nTitle: Puppy playing at park - other owner aggressive towards him [help]\n\nText: Hi all, looking for some advice. [...] that too.\n\nTL;DR:",
"generated_summary_for_comparison_A":"New dog at park is being aggressive to my pup, owner won't stop. What do I do?",
"generated_summary_for_comparison_B":"A new dog has been coming to the dog park and the first day the new dog came, the old dog (a kelpie) was all over him.",
"generated_summary_for_feedback":"A new dog has been coming to the dog park and the first day the owner hauled buzz off and whacked him. Today, the owner was staring daggers at me and lunging at buzz\/pulling his collar roughly.",
"comparison_preference":"Summary A",
"feedback":"The summary is concise but could include information about the poster knowing the dogs are just playing and will react if they become aggressive and wants to know how to handle things with Max's dad. ",
"feedback_class":"Coverage",
"has_additional_feedback":"No",
"ideal_human_summary":"The poster is frustrated with a new person at the dog park who is upset with him because their young dogs are playing roughly. The poster will step in if it gets aggressive and wants the new person to understand this. "}
```
There are some additional fields like `time_spent_in_seconds_ideal_human_summary`, `time_spent_in_seconds_feedback`,`time_spent_in_seconds_comparison` which only have values for the development dataset.
### Data Fields
- `id`: a unique string identifying the reddit post.
- `subreddit`: subreddit of the post.
- `title`: title of the reddit post.
- `post`: reddit post
- `tldr_human_reference_summary`: human reference summary automatically extracted from reddit (taken from the dataset of [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf))
- `summary_prompt`: the whole prompt used to generate summaries
- `generated_summary_for_comparison_A`: summary A used for binary human comparison (generated with FeedME)
- `generated_summary_for_comparison_B`: summary B used for binary human comparison (generated with FeedME)
- `generated_summary_for_feedback`: summary used to gather human language feedback ((generated with FeedME))
- `comparison_preference`: prefered Summary of human comparison, Values: "Summary A", "Summary B"
- `feedback`: human language feedback on `generated_summary_for_feedback`(most important feedback point)
- `feedback_class`: Class of language feedback, Values: "Coverage", "Accuracy", "Coherence", "other"
- `has_additional_feedback`: Whether this sample could use more feedback on an important point.
- `ideal_human_summary`: high-quality human-written summary for this sample. We instructed annotators to write an ideal summary.
- `time_spent_in_seconds_ideal_human_summary`: Annotation time for ideal human summary
- `time_spent_in_seconds_feedback`: Annotation time for language feedback
- `time_spent_in_seconds_comparison`: Annotation time for binary comparison
Note that the various datasplits have varying fields. The fields that are not contained in a dataset have the value None.
### Data Splits
The SLF5K dataset has 4 splits: _train_, _development_, _validation_, and _test_. Below are the statistics of the dataset.
| Dataset Split | Number of Instances in Split |
| ------------- | ------------------------------------------- |
| Train | 5000 |
| Development | 200 |
| Validation | 500 |
| Test | 698 |
The reason we introduce a development and validation dataset, is the following.
## Dataset Creation
### Curation Rationale
This dataset aims to support supervised language model training from human preferences on a summarization task with real natural training data.
### Source Data
#### Initial Data Collection and Normalization
The initial TL;DR dataset was made public by Völkse et. al. in the paper [TL;DR: Mining Reddit to Learn Automatic Summarization](https://aclanthology.org/W17-4508.pdf) (licensed under CC By 4.0).
Stiennon et. al. then use this TL;DR dataset for their work [Learning to Summarize from Human Feedbback](https://arxiv.org/pdf/2009.01325.pdf).
They filter the TL;DR dataset for quality reasons and collect binary human preference labels.
Our datset is a subset from Stiennon et. al. Dataset, which can be downloaded [here](https://github.com/openai/summarize-from-feedback).
Our train and development dataset are taken form their train dataset and our test and validation datasets are taken from their test datasest.
#### Who are the source language producers?
The reddit posts are written by users of reddit.com.
### Annotations
#### Annotation process
We first onboarded annotators by giving them test tasks on which we evaluated their annotation quality. We then selected 31
annotators for the remainder of the project (a few were removed later on due to quality issues). Througout the process
we updated our instructions to make the tasks clearer and stayed in close contact with the annotators to answer questions etc.
The various dataset splits were collected in multiple annotation iterations. The largest annotation was a single iteration of annotation
5000 samples for the train dataset.
#### Who are the annotators?
We used annotators through the annotation service [Surge AI](https://www.surgehq.ai/).
### Personal and Sensitive Information
The annotators were completely anonymized and no information about them can be found in the dataset.
## Considerations for Using the Data
### Social Impact of Dataset
The purpose of this dataset is to align language models with human preferences by leveraging language feedback, on the task of summarization. Concretely, the goal is to
to develop models that produce summaries for reddit posts that are more in line with human preferences.
Note that this does not imply that the outputs will perfectly be aligned with human values, i.e. outputs can still be misaligned, offensive and contain harumful biases.
While outputs from a model trained on our dataset may reflect the language of the reddit posts, summaries, and human feedback, it should always be made clear that such an output
is automatically generated.
### Discussion of Biases
The TL;DR dataset consists of user-submitted posts to the website reddit.com. It can thus contain content that is offensive or reflects harmful social biases.
We thus recommend that models trained on the SLF5K dataset (which is based on the TL;DR) dataset be thoroughly studied for potential harmful behavior.
The human preferences and feedback represented in this dataset were collected through crowd-workers and may disproportionally represent the views, biases, and values
of the respective demographic of the annotators.
### Other Known Limitations
The "human-summaries" collected in the TL;DR dataset (and available in the SLF5K dataset under the field `tldr_human_reference_summary`, were automatically extracted from reddit.com.
They are often of poor quality and do not accurately reflect human summarization performance. In our paper, we show that our human written summaries (available in the SLF5K dataset under the field
`ideal_human_summary`) are of much higher quality.
## Additional Information
### Dataset Curators
The data is collected by Jérémy Scheurer, Jon Ander Campos, Tomasz Korbak, Jun Shern Chan, Angelica Chen, Kyunghyun Cho, and Ethan Perez.
All authors are affiliated with New York University. Additionally, Jérémy Scheurer is affiliated with FAR AI. Jon Ander
is affiliated with the University of the Basque Country. Tomek Korbak is affiliated with FAR AI and the University of Sussesx.
Kyunghyun Cho is affiliated with Genentech and CIFAR LMB. Ethan Perez is affiliated with FAR AI and Anthropic.
### Licensing Information
The SLF5K dataset is released under the Apache 2.0 license.
### Citation Information
TBD |
emozilla/govreport-test-tokenized | 2023-08-09T02:35:24.000Z | [
"region:us"
] | emozilla | null | null | null | 0 | 174 | ---
dataset_info:
features:
- name: id
dtype: string
- name: pid
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
- name: tokenized_len
dtype: int64
splits:
- name: test
num_bytes: 107857269
num_examples: 973
download_size: 43840982
dataset_size: 107857269
---
# Dataset Card for "govreport-test-tokenized"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
ura-hcmut/MATH | 2023-09-29T17:19:11.000Z | [
"task_categories:text2text-generation",
"language:vi",
"license:cc-by-nc-sa-4.0",
"region:us"
] | ura-hcmut | null | null | null | 0 | 174 | ---
license: cc-by-nc-sa-4.0
task_categories:
- text2text-generation
language:
- vi
configs:
- config_name: gcp
data_files:
- split: train
path: "MATH_gcp_training.csv"
- split: test
path: "MATH_gcp.csv"
- config_name: azr
data_files:
- split: train
path: "MATH_azr_training.csv"
- split: test
path: "MATH_azr.csv"
---
# MATH dataset
Original version: https://huggingface.co/datasets/lighteval/MATH
Translation source code: https://github.com/martinakaduc/ura-llama/tree/main/dataset_scripts/custom_datasets
|
conceptual_12m | 2022-11-03T16:31:22.000Z | [
"task_categories:image-to-text",
"task_ids:image-captioning",
"annotations_creators:found",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:10M<n<100M",
"source_datasets:original",
"language:en",
"license:other",
"arxiv:2102.08981",
"region:us"
] | null | Conceptual 12M is a large-scale dataset of 12 million
image-text pairs specifically meant to be used for visionand-language pre-training.
Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M. | @inproceedings{changpinyo2021cc12m,
title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
booktitle = {CVPR},
year = {2021},
} | null | 10 | 173 | ---
annotations_creators:
- found
language_creators:
- found
language:
- en
license:
- other
multilinguality:
- monolingual
size_categories:
- 10M<n<100M
source_datasets:
- original
task_categories:
- image-to-text
task_ids:
- image-captioning
paperswithcode_id: cc12m
pretty_name: Conceptual 12M
dataset_info:
features:
- name: image_url
dtype: string
- name: caption
dtype: string
splits:
- name: train
num_bytes: 2794168030
num_examples: 12423374
download_size: 2707204412
dataset_size: 2794168030
---
# Dataset Card for Conceptual 12M
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Dataset Preprocessing](#dataset-preprocessing)
- [Supported Tasks](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-instances)
- [Data Splits](#data-instances)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
## Dataset Description
- **Repository:** [Conceptual 12M repository](https://github.com/google-research-datasets/conceptual-12m)
- **Paper:** [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://arxiv.org/abs/2102.08981)
- **Point of Contact:** [Conceptual Captions e-mail](mailto:conceptual-captions@google.com)
### Dataset Summary
Conceptual 12M (CC12M) is a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training.
Its data collection pipeline is a relaxed version of the one used in Conceptual Captions 3M (CC3M).
### Dataset Preprocessing
This dataset doesn't download the images locally by default. Instead, it exposes URLs to the images. To fetch the images, use the following code:
```python
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import io
import urllib
import PIL.Image
from datasets import load_dataset
from datasets.utils.file_utils import get_datasets_user_agent
USER_AGENT = get_datasets_user_agent()
def fetch_single_image(image_url, timeout=None, retries=0):
for _ in range(retries + 1):
try:
request = urllib.request.Request(
image_url,
data=None,
headers={"user-agent": USER_AGENT},
)
with urllib.request.urlopen(request, timeout=timeout) as req:
image = PIL.Image.open(io.BytesIO(req.read()))
break
except Exception:
image = None
return image
def fetch_images(batch, num_threads, timeout=None, retries=0):
fetch_single_image_with_args = partial(fetch_single_image, timeout=timeout, retries=retries)
with ThreadPoolExecutor(max_workers=num_threads) as executor:
batch["image"] = list(executor.map(fetch_single_image_with_args, batch["image_url"]))
return batch
num_threads = 20
dset = load_dataset("conceptual_12m")
dset = dset.map(fetch_images, batched=True, batch_size=100, fn_kwargs={"num_threads": num_threads})
```
### Supported Tasks and Leaderboards
- `image-captioning`: This dataset can be used to train model for the Image Captioning task.
### Languages
All captions are in English.
## Dataset Structure
### Data Instances
Each instance represents a single image with a caption:
```
{
'image_url': 'http://lh6.ggpht.com/-IvRtNLNcG8o/TpFyrudaT6I/AAAAAAAAM6o/_11MuAAKalQ/IMG_3422.JPG?imgmax=800',
'caption': 'a very typical bus station'
}
```
### Data Fields
- `image_url`: Static URL for downloading the image associated with the post.
- `caption`: Textual description of the image.
### Data Splits
There is only training data, with a total of 12423374 rows
## Dataset Creation
### Curation Rationale
Conceptual 12M shares the same pipeline with Conceptual Captions (CC3M), but relaxes some processing steps.
### Source Data
#### Initial Data Collection and Normalization
From the paper:
> To arrive at CC12M, we keep
the image-text filtering intact, and relax the unimodal filters only. First, for image-based filtering, we set the maximum ratio of larger to smaller dimension to 2.5 instead of 2.
We still keep only JPEG images with size greater than
400 pixels, and still exclude images that trigger pornography detectors. Second, in text-based filtering, we allow text
between 3 and 256 words in the alt-text. We still discard
candidates with no noun or no determiner, but permit ones
without prepositions. We discard the heuristics regarding
high unique-word ratio covering various POS tags and word
capitalization. We set the maximum fraction of word repetition allowed to 0.2. Given a larger pool of text due to the
above relaxations, the threshold for counting a word type as
rare is increased from 5 to 20
> The main motivation for CC3M to
perform text transformation is that a majority of candidate
captions contain ultrafine-grained entities such as proper
names (people, venues, locations, etc.), making it extremely
difficult to learn as part of the image captioning task. In
contrast, we are not restricted by the end task of image caption generation. Our intuition is that relatively more difficult pre-training data would lead to better transferability.
We thus do not perform hypernimization or digit substitution. [...] The only exception to the “keep alt-texts as
raw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy
of the individuals in these images. For this step, we use the
Google Cloud Natural Language APIs to detect all named
entities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M
are transformed in this fashion.
#### Who are the source language producers?
Not specified.
### Annotations
#### Annotation process
Annotations are extracted jointly with the images using the automatic pipeline.
#### Who are the annotators?
Not specified.
### Personal and Sensitive Information
From the paper:
> The only exception to the “keep alt-texts as
raw as possible” rule is performing person-name substitutions, which we identify as necessary to protect the privacy
of the individuals in these images. For this step, we use the
Google Cloud Natural Language APIs to detect all named
entities of type Person, and substitute them by a special token <PERSON>. Around 25% of all the alt-texts in CC12M
are transformed in this fashion.
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
Soravit Changpinyo, Piyush Sharma, Nan Ding and Radu Soricut.
### Licensing Information
The dataset may be freely used for any purpose, although acknowledgement of
Google LLC ("Google") as the data source would be appreciated. The dataset is
provided "AS IS" without any warranty, express or implied. Google disclaims all
liability for any damages, direct or indirect, resulting from the use of the
dataset.
### Citation Information
```bibtex
@inproceedings{changpinyo2021cc12m,
title = {{Conceptual 12M}: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts},
author = {Changpinyo, Soravit and Sharma, Piyush and Ding, Nan and Soricut, Radu},
booktitle = {CVPR},
year = {2021},
}
```
### Contributions
Thanks to [@thomasw21](https://github.com/thomasw21) for adding this dataset. |
florentgbelidji/car-reviews | 2022-06-08T16:43:39.000Z | [
"region:us"
] | florentgbelidji | null | null | null | 0 | 173 | Entry not found |
tomekkorbak/detoxify-pile-chunk3-50000-100000 | 2022-10-06T02:59:17.000Z | [
"region:us"
] | tomekkorbak | null | null | null | 0 | 173 | Entry not found |
Jean-Baptiste/financial_news_sentiment | 2022-12-29T03:14:44.000Z | [
"task_categories:text-classification",
"task_ids:multi-class-classification",
"task_ids:sentiment-classification",
"annotations_creators:expert-generated",
"multilinguality:monolingual",
"size_categories:1K<n<10K",
"language:en",
"license:mit",
"region:us"
] | Jean-Baptiste | null | null | null | 6 | 173 | ---
language:
- en
dataset_info:
splits:
- name: test
num_examples: 267
- name: train
num_examples: 1512
annotations_creators:
- expert-generated
license:
- mit
multilinguality:
- monolingual
pretty_name: financial_news_sentiment
size_categories:
- 1K<n<10K
tags: []
task_categories:
- text-classification
task_ids:
- multi-class-classification
- sentiment-classification
---
# Dataset Card for "financial_news_sentiment"
Manually validated sentiment for ~2000 Canadian news articles.
The dataset also include a column topic which contains one of the following value:
* acquisition
* other
* quaterly financial release
* appointment to new position
* dividend
* corporate update
* drillings results
* conference
* share repurchase program
* grant of stocks
This was generated automatically using a zero-shot classification model and **was not** reviewed manually. |
Tuana/presidents | 2023-02-28T01:06:47.000Z | [
"region:us"
] | Tuana | null | null | null | 1 | 173 | ---
dataset_info:
features:
- name: id
dtype: string
- name: content
dtype: string
- name: content_type
dtype: string
- name: meta
struct:
- name: url
dtype: string
- name: _split_id
dtype: int64
- name: id_hash_keys
sequence: string
- name: score
dtype: 'null'
- name: embedding
dtype: 'null'
splits:
- name: train
num_bytes: 9366886
num_examples: 5529
download_size: 4997888
dataset_size: 9366886
---
# Dataset Card for "presidents"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
casehold/casehold | 2023-10-04T19:55:29.000Z | [
"region:us"
] | casehold | CaseHOLD (Case Holdings On Legal Decisions) is a law dataset comprised of over 53,000+ multiple choice questions to identify the relevant holding of a cited case. | @inproceedings{zhengguha2021,
title={When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset},
author={Lucia Zheng and Neel Guha and Brandon R. Anderson and Peter Henderson and Daniel E. Ho},
year={2021},
eprint={2104.08671},
archivePrefix={arXiv},
primaryClass={cs.CL},
booktitle={Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law},
publisher={Association for Computing Machinery}
} | null | 4 | 173 | Entry not found |
aboonaji/alpaca_micro_demo | 2023-08-08T13:57:18.000Z | [
"region:us"
] | aboonaji | null | null | null | 0 | 173 | Entry not found |
ziozzang/EverythingLM-data-V2-Ko | 2023-08-23T07:03:47.000Z | [
"language:ko",
"license:mit",
"region:us"
] | ziozzang | null | null | null | 7 | 173 | ---
license: mit
language:
- ko
---
# Translated into Korean with DeepL
All Texts are translated with DeepL. (Machine Translated.)
- Issue: some data items are missing, cause of DeepL plan and processing method. I use very cheap plan and all datas are merged into single file and splitted by few code and hand.
- This is sample/test processing of data set creation with DeepL.
- Original Dataset: totally-not-an-llm/EverythingLM-data-V2
# EverythingLM V2 Dataset
**EverythingLM V2** is a diverse instruct dataset consisting of 1k of human-assistant conversations. These sets were generated using principles from both evol-instruct and Orca. The dataset encompasses a wide array of topics and interactions.
### Differences for V1:
- All data in V2 is generated by GPT4
- Higher quality dataset generation pipeline:
- More humalike seed prompts
- Fixed some bugs in the script
- More diverse creative writing
- More diverse seed prompts in general
- Attempt not to overfit the model on complex instructions by occasionally skipping evol
### Cost:
Reproducing this dataset would cost roughly $40.
### Instruction Categories:
- Reasoning
- Creative Writing
- General Knowledge
- Brainstorming
- Search Query
- Coding
- Basic Instruct
We also leverage various system prompts for evol-instruct and for responding to prompts.
This dataset has also been filtered to remove OpenAI alignment.
### How it stands out:
- Long, detailed outputs
- Humanlike creativity
- CoT reasoning
- Complex & challenging tasks
### Plans:
- Train Llama 7b & 13b models (13b model V1 trained)
- Train Llama 70b QLoRA
- Generate V2 of the dataset, with more categories and GPT-4 (DONE) ✓
Included in this repo is the script to generate the dataset. |
vlsp-2023-vllm/arithmetic_vi | 2023-09-19T03:54:17.000Z | [
"arxiv:2005.14165",
"region:us"
] | vlsp-2023-vllm | null | null | null | 0 | 173 | ---
dataset_info:
features:
- name: context
dtype: string
- name: completion
dtype: string
- name: meta
dtype: string
splits:
- name: test
num_bytes: 1729595
num_examples: 26000
download_size: 515170
dataset_size: 1729595
---
# Arithmetic (OpenAI)
Source: https://github.com/openai/gpt-3
Vietnamese version of Arithmetic.
## Citation Information
```
@article{brown2020language,
title={Language Models are Few-Shot Learners},
author={Tom B. Brown and Benjamin Mann and Nick Ryder and Melanie Subbiah and Jared Kaplan and Prafulla Dhariwal and Arvind Neelakantan and Pranav Shyam and Girish Sastry and Amanda Askell and Sandhini Agarwal and Ariel Herbert-Voss and Gretchen Krueger and Tom Henighan and Rewon Child and Aditya Ramesh and Daniel M. Ziegler and Jeffrey Wu and Clemens Winter and Christopher Hesse and Mark Chen and Eric Sigler and Mateusz Litwin and Scott Gray and Benjamin Chess and Jack Clark and Christopher Berner and Sam McCandlish and Alec Radford and Ilya Sutskever and Dario Amodei},
year={2020},
eprint={2005.14165},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
NamCyan/Evol-TheVault | 2023-09-15T18:04:10.000Z | [
"region:us"
] | NamCyan | null | null | null | 0 | 173 | ---
dataset_info:
features:
- name: id
dtype: int64
- name: instruction
dtype: string
- name: code
dtype: string
- name: tokenized_instruction
sequence: string
- name: type
dtype: string
- name: language
dtype: string
splits:
- name: train
num_bytes: 175466743
num_examples: 47797
download_size: 55571461
dataset_size: 175466743
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "Evol-TheVault"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
great_code | 2022-11-18T20:05:00.000Z | [
"task_categories:table-to-text",
"annotations_creators:expert-generated",
"language_creators:found",
"multilinguality:monolingual",
"size_categories:1M<n<10M",
"source_datasets:original",
"language:en",
"license:cc-by-sa-3.0",
"region:us"
] | null | The dataset for the variable-misuse task, described in the ICLR 2020 paper 'Global Relational Models of Source Code' [https://openreview.net/forum?id=B1lnbRNtwr]
This is the public version of the dataset used in that paper. The original, used to produce the graphs in the paper, could not be open-sourced due to licensing issues. See the public associated code repository [https://github.com/VHellendoorn/ICLR20-Great] for results produced from this dataset.
This dataset was generated synthetically from the corpus of Python code in the ETH Py150 Open dataset [https://github.com/google-research-datasets/eth_py150_open]. | @inproceedings{DBLP:conf/iclr/HellendoornSSMB20,
author = {Vincent J. Hellendoorn and
Charles Sutton and
Rishabh Singh and
Petros Maniatis and
David Bieber},
title = {Global Relational Models of Source Code},
booktitle = {8th International Conference on Learning Representations, {ICLR} 2020,
Addis Ababa, Ethiopia, April 26-30, 2020},
publisher = {OpenReview.net},
year = {2020},
url = {https://openreview.net/forum?id=B1lnbRNtwr},
timestamp = {Thu, 07 May 2020 17:11:47 +0200},
biburl = {https://dblp.org/rec/conf/iclr/HellendoornSSMB20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
} | null | 1 | 172 | ---
annotations_creators:
- expert-generated
language_creators:
- found
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- monolingual
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
- table-to-text
task_ids: []
paperswithcode_id: null
pretty_name: GREAT
dataset_info:
features:
- name: id
dtype: int32
- name: source_tokens
sequence: string
- name: has_bug
dtype: bool
- name: error_location
dtype: int32
- name: repair_candidates
sequence: string
- name: bug_kind
dtype: int32
- name: bug_kind_name
dtype: string
- name: repair_targets
sequence: int32
- name: edges
list:
list:
- name: before_index
dtype: int32
- name: after_index
dtype: int32
- name: edge_type
dtype: int32
- name: edge_type_name
dtype: string
- name: provenances
list:
- name: datasetProvenance
struct:
- name: datasetName
dtype: string
- name: filepath
dtype: string
- name: license
dtype: string
- name: note
dtype: string
splits:
- name: train
num_bytes: 14705534822
num_examples: 1798742
- name: validation
num_bytes: 1502956919
num_examples: 185656
- name: test
num_bytes: 7880762248
num_examples: 968592
download_size: 23310374002
dataset_size: 24089253989
---
# Dataset Card for GREAT
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** None
- **Repository:** https://github.com/google-research-datasets/great
- **Paper:** https://openreview.net/forum?id=B1lnbRNtwr
- **Leaderboard:** [More Information Needed]
- **Point of Contact:** [More Information Needed]
### Dataset Summary
[More Information Needed]
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
[More Information Needed]
## Dataset Structure
### Data Instances
Here are some examples of questions and facts:
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@abhishekkrthakur](https://github.com/abhishekkrthakur) for adding this dataset. |
flexthink/librig2p-nostress-space | 2022-06-24T01:23:49.000Z | [
"region:us"
] | flexthink | Grapheme-to-Phoneme training, validation and test sets | null | null | 0 | 172 | # librig2p-nostress - Grapheme-To-Phoneme Dataset
This dataset contains samples that can be used to train a Grapheme-to-Phoneme system **without** stress information.
The dataset is derived from the following pre-existing datasets:
* [LibriSpeech ASR Corpus](https://www.openslr.org/12)
* [LibriSpeech Alignments](https://github.com/CorentinJ/librispeech-alignments)
* [Wikipedia Homograph Disambiguation Data](https://github.com/google/WikipediaHomographData) |
eloukas/edgar-corpus | 2023-07-14T07:17:12.000Z | [
"task_categories:other",
"annotations_creators:no-annotation",
"language_creators:other",
"multilinguality:monolingual",
"size_categories:100K<n<1M",
"source_datasets:extended|other",
"language:en",
"license:apache-2.0",
"research papers",
"edgar",
"sec",
"finance",
"financial",
"filings",
"10K",
"10-K",
"nlp",
"research",
"econlp",
"economics",
"business",
"arxiv:2109.14394",
"region:us"
] | eloukas | The dataset contains annual filings (10K) of all publicly traded firms from 1993-2020. The table data is stripped but all text is retained.
This dataset allows easy access to the EDGAR-CORPUS dataset based on the paper EDGAR-CORPUS: Billions of Tokens Make The World Go Round (See References in README.md for details). | null | null | 16 | 172 | ---
dataset_info:
- config_name: .
features:
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dtype: string
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splits:
- name: train
num_bytes: 40306320885
num_examples: 220375
download_size: 10734208660
dataset_size: 40306320885
- config_name: full
features:
- name: filename
dtype: string
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dtype: string
- name: year
dtype: string
- name: section_1
dtype: string
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license:
- apache-2.0
multilinguality:
- monolingual
pretty_name: EDGAR-CORPUS (10-K Filings from 1999 to 2020)
size_categories:
- 100K<n<1M
source_datasets:
- extended|other
tags:
- research papers
- edgar
- sec
- finance
- financial
- filings
- 10K
- 10-K
- nlp
- research
- econlp
- economics
- business
task_categories:
- other
task_ids: []
---
# Dataset Card for [EDGAR-CORPUS]
## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks](#supported-tasks)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Licensing Information](#licensing-information)
- [References](#references)
- [Contributions](#contributions)
## Dataset Description
- **Point of Contact: Lefteris Loukas**
### Dataset Summary
This dataset card is based on the paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round** authored by _Lefteris Loukas et.al_, as published in the _ECONLP 2021_ workshop.
This dataset contains the annual reports of public companies from 1993-2020 from SEC EDGAR filings.
There is supported functionality to load a specific year.
Care: since this is a corpus dataset, different `train/val/test` splits do not have any special meaning. It's the default HF card format to have train/val/test splits.
If you wish to load specific year(s) of specific companies, you probably want to use the open-source software which generated this dataset, EDGAR-CRAWLER: https://github.com/nlpaueb/edgar-crawler.
## Citation
If this work helps or inspires you in any way, please consider citing the relevant paper published at the [3rd Economics and Natural Language Processing (ECONLP) workshop](https://lt3.ugent.be/econlp/) at EMNLP 2021 (Punta Cana, Dominican Republic):
```
@inproceedings{loukas-etal-2021-edgar,
title = "{EDGAR}-{CORPUS}: Billions of Tokens Make The World Go Round",
author = "Loukas, Lefteris and
Fergadiotis, Manos and
Androutsopoulos, Ion and
Malakasiotis, Prodromos",
booktitle = "Proceedings of the Third Workshop on Economics and Natural Language Processing",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.econlp-1.2",
pages = "13--18",
}
```
### Supported Tasks
This is a raw dataset/corpus for financial NLP.
As such, there are no annotations or labels.
### Languages
The EDGAR Filings are in English.
## Dataset Structure
### Data Instances
Refer to the dataset preview.
### Data Fields
**filename**: Name of file on EDGAR from which the report was extracted.<br>
**cik**: EDGAR identifier for a firm.<br>
**year**: Year of report.<br>
**section_1**: Corressponding section of the Annual Report.<br>
**section_1A**: Corressponding section of the Annual Report.<br>
**section_1B**: Corressponding section of the Annual Report.<br>
**section_2**: Corressponding section of the Annual Report.<br>
**section_3**: Corressponding section of the Annual Report.<br>
**section_4**: Corressponding section of the Annual Report.<br>
**section_5**: Corressponding section of the Annual Report.<br>
**section_6**: Corressponding section of the Annual Report.<br>
**section_7**: Corressponding section of the Annual Report.<br>
**section_7A**: Corressponding section of the Annual Report.<br>
**section_8**: Corressponding section of the Annual Report.<br>
**section_9**: Corressponding section of the Annual Report.<br>
**section_9A**: Corressponding section of the Annual Report.<br>
**section_9B**: Corressponding section of the Annual Report.<br>
**section_10**: Corressponding section of the Annual Report.<br>
**section_11**: Corressponding section of the Annual Report.<br>
**section_12**: Corressponding section of the Annual Report.<br>
**section_13**: Corressponding section of the Annual Report.<br>
**section_14**: Corressponding section of the Annual Report.<br>
**section_15**: Corressponding section of the Annual Report.<br>
```python
import datasets
# Load the entire dataset
raw_dataset = datasets.load_dataset("eloukas/edgar-corpus", "full")
# Load a specific year and split
year_1993_training_dataset = datasets.load_dataset("eloukas/edgar-corpus", "year_1993", split="train")
```
### Data Splits
| Config | Training | Validation | Test |
| --------- | -------- | ---------- | ------ |
| full | 176,289 | 22,050 | 22,036 |
| year_1993 | 1,060 | 133 | 133 |
| year_1994 | 2,083 | 261 | 260 |
| year_1995 | 4,110 | 514 | 514 |
| year_1996 | 7,589 | 949 | 949 |
| year_1997 | 8,084 | 1,011 | 1,011 |
| year_1998 | 8,040 | 1,006 | 1,005 |
| year_1999 | 7,864 | 984 | 983 |
| year_2000 | 7,589 | 949 | 949 |
| year_2001 | 7,181 | 898 | 898 |
| year_2002 | 6,636 | 830 | 829 |
| year_2003 | 6,672 | 834 | 834 |
| year_2004 | 7,111 | 889 | 889 |
| year_2005 | 7,113 | 890 | 889 |
| year_2006 | 7,064 | 883 | 883 |
| year_2007 | 6,683 | 836 | 835 |
| year_2008 | 7,408 | 927 | 926 |
| year_2009 | 7,336 | 917 | 917 |
| year_2010 | 7,013 | 877 | 877 |
| year_2011 | 6,724 | 841 | 840 |
| year_2012 | 6,479 | 810 | 810 |
| year_2013 | 6,372 | 797 | 796 |
| year_2014 | 6,261 | 783 | 783 |
| year_2015 | 6,028 | 754 | 753 |
| year_2016 | 5,812 | 727 | 727 |
| year_2017 | 5,635 | 705 | 704 |
| year_2018 | 5,508 | 689 | 688 |
| year_2019 | 5,354 | 670 | 669 |
| year_2020 | 5,480 | 686 | 685 |
## Dataset Creation
### Source Data
#### Initial Data Collection and Normalization
Initial data was collected and processed by the authors of the research paper **EDGAR-CORPUS: Billions of Tokens Make The World Go Round**.
#### Who are the source language producers?
Public firms filing with the SEC.
### Annotations
#### Annotation process
NA
#### Who are the annotators?
NA
### Personal and Sensitive Information
The dataset contains public filings data from SEC.
## Considerations for Using the Data
### Social Impact of Dataset
Low to none.
### Discussion of Biases
The dataset is about financial information of public companies and as such the tone and style of text is in line with financial literature.
### Other Known Limitations
The dataset needs further cleaning for improved performance.
## Additional Information
### Licensing Information
EDGAR data is publicly available.
### Shoutout
Huge shoutout to [@JanosAudran](https://huggingface.co/JanosAudran) for the HF Card setup!
### References
- [Research Paper] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CORPUS: Billions of Tokens Make The World Go Round. Third Workshop on Economics and Natural Language Processing (ECONLP). https://arxiv.org/abs/2109.14394 - Punta Cana, Dominican Republic, November 2021.
- [Software] Lefteris Loukas, Manos Fergadiotis, Ion Androutsopoulos, and, Prodromos Malakasiotis. EDGAR-CRAWLER. https://github.com/nlpaueb/edgar-crawler (2021)
- [EDGAR CORPUS, but in zip files] EDGAR CORPUS: A corpus for financial NLP research, built from SEC's EDGAR. https://zenodo.org/record/5528490 (2021)
- [Word Embeddings] EDGAR-W2V: Word2vec Embeddings trained on EDGAR-CORPUS. https://zenodo.org/record/5524358 (2021)
- [Applied Research paper where EDGAR-CORPUS is used] Lefteris Loukas, Manos Fergadiotis, Ilias Chalkidis, Eirini Spyropoulou, Prodromos Malakasiotis, Ion Androutsopoulos, and, George Paliouras. FiNER: Financial Numeric Entity Recognition for XBRL Tagging. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). https://doi.org/10.18653/v1/2022.acl-long.303 (2022)
|
commaai/commavq | 2023-09-19T21:38:38.000Z | [
"size_categories:100K<n<1M",
"license:mit",
"region:us"
] | commaai | TODO | null | null | 9 | 172 | ---
license: mit
size_categories:
- 100K<n<1M
---
# commaVQ
commaVQ is a dataset of 100,000 heavily compressed driving videos for Machine Learning research. A heavily compressed driving video like this is useful to experiment with GPT-like video prediction models. This repo includes an encoder/decoder and an example of a video prediction model.
Examples and trained models can be found here: https://github.com/commaai/commavq
# Overview
A VQ-VAE [1,2] was used to heavily compress each frame into 128 "tokens" of 10 bits each. Each entry of the dataset is a "segment" of compressed driving video, i.e. 1min of frames at 20 FPS. Each file is of shape 1200x8x16 and saved as int16.
Note that the compressor is extremely lossy on purpose. It makes the dataset smaller and easy to play with (train GPT with large context size, fast autoregressive generation, etc.). We might extend the dataset to a less lossy version when we see fit.
<video title="source" controls>
<source src="https://github.com/commaai/commavq/assets/29985433/91894bf7-592b-4204-b3f2-3e805984045c" type="video/mp4">
</video>
<video title="compressed" controls>
<source src="https://github.com/commaai/commavq/assets/29985433/3a799ac8-781e-461c-bf14-c15cea42b985" type="video/mp4">
</video>
<video title="imagined" controls>
<source src="https://github.com/commaai/commavq/assets/29985433/f6f7699b-b6cb-4f9c-80c9-8e00d75fbfae" type="video/mp4">
</video>
# References
[1] Van Den Oord, Aaron, and Oriol Vinyals. "Neural discrete representation learning." Advances in neural information processing systems 30 (2017).
[2] Esser, Patrick, Robin Rombach, and Bjorn Ommer. "Taming transformers for high-resolution image synthesis." Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021. |
jason-lee08/TinyStoriesWithExclamationsSmall | 2023-08-20T03:52:44.000Z | [
"region:us"
] | jason-lee08 | null | null | null | 0 | 172 | ---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
dataset_info:
features:
- name: input_ids
sequence: int32
- name: attention_mask
sequence: int8
splits:
- name: train
num_bytes: 23826331
num_examples: 21197
- name: validation
num_bytes: 236180
num_examples: 220
download_size: 8127925
dataset_size: 24062511
---
# Dataset Card for "TinyStoriesWithExclamationsSmall"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) |
catalonia_independence | 2023-06-01T14:59:47.000Z | [
"task_categories:text-classification",
"annotations_creators:expert-generated",
"language_creators:crowdsourced",
"multilinguality:monolingual",
"size_categories:10K<n<100K",
"source_datasets:original",
"language:ca",
"language:es",
"license:cc-by-nc-sa-4.0",
"stance-detection",
"region:us"
] | null | This dataset contains two corpora in Spanish and Catalan that consist of annotated Twitter messages for automatic stance detection. The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia.
Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance towards the target - independence of Catalonia. | @inproceedings{zotova-etal-2020-multilingual,
title = "Multilingual Stance Detection in Tweets: The {C}atalonia Independence Corpus",
author = "Zotova, Elena and
Agerri, Rodrigo and
Nunez, Manuel and
Rigau, German",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://www.aclweb.org/anthology/2020.lrec-1.171",
pages = "1368--1375",
abstract = "Stance detection aims to determine the attitude of a given text with respect to a specific topic or claim. While stance detection has been fairly well researched in the last years, most the work has been focused on English. This is mainly due to the relative lack of annotated data in other languages. The TW-10 referendum Dataset released at IberEval 2018 is a previous effort to provide multilingual stance-annotated data in Catalan and Spanish. Unfortunately, the TW-10 Catalan subset is extremely imbalanced. This paper addresses these issues by presenting a new multilingual dataset for stance detection in Twitter for the Catalan and Spanish languages, with the aim of facilitating research on stance detection in multilingual and cross-lingual settings. The dataset is annotated with stance towards one topic, namely, the ndependence of Catalonia. We also provide a semi-automatic method to annotate the dataset based on a categorization of Twitter users. We experiment on the new corpus with a number of supervised approaches, including linear classifiers and deep learning methods. Comparison of our new corpus with the with the TW-1O dataset shows both the benefits and potential of a well balanced corpus for multilingual and cross-lingual research on stance detection. Finally, we establish new state-of-the-art results on the TW-10 dataset, both for Catalan and Spanish.",
language = "English",
ISBN = "979-10-95546-34-4",
} | null | 1 | 171 | ---
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
language:
- ca
- es
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- text-classification
task_ids: []
paperswithcode_id: cic
pretty_name: Catalonia Independence Corpus
tags:
- stance-detection
dataset_info:
- config_name: catalan
features:
- name: id_str
dtype: string
- name: TWEET
dtype: string
- name: LABEL
dtype:
class_label:
names:
'0': AGAINST
'1': FAVOR
'2': NEUTRAL
splits:
- name: train
num_bytes: 1406250
num_examples: 6028
- name: test
num_bytes: 469204
num_examples: 2010
- name: validation
num_bytes: 473393
num_examples: 2010
download_size: 995415
dataset_size: 2348847
- config_name: spanish
features:
- name: id_str
dtype: string
- name: TWEET
dtype: string
- name: LABEL
dtype:
class_label:
names:
'0': AGAINST
'1': FAVOR
'2': NEUTRAL
splits:
- name: train
num_bytes: 1507388
num_examples: 6046
- name: test
num_bytes: 501783
num_examples: 2016
- name: validation
num_bytes: 505092
num_examples: 2015
download_size: 1070281
dataset_size: 2514263
config_names:
- catalan
- spanish
---
# Dataset Card for Catalonia Independence Corpus
## Table of Contents
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Annotations](#annotations)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/ixa-ehu/catalonia-independence-corpus
- **Repository:** https://github.com/ixa-ehu/catalonia-independence-corpus
- **Paper:** [Multilingual Stance Detection: The Catalonia Independence Corpus](https://www.aclweb.org/anthology/2020.lrec-1.171/)
- **Leaderboard:**
- **Point of Contact:** [Rodrigo Agerri](https://github.com/ragerri) (corpus creator)
### Dataset Summary
This dataset contains two corpora in Spanish and Catalan that consist of annotated Twitter messages for automatic stance detection. The data was collected over 12 days during February and March of 2019 from tweets posted in Barcelona, and during September of 2018 from tweets posted in the town of Terrassa, Catalonia.
Each corpus is annotated with three classes: AGAINST, FAVOR and NEUTRAL, which express the stance towards the target - independence of Catalonia.
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
Spanish and Catalan
## Dataset Structure
### Data Instances
[More Information Needed]
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
[More Information Needed]
### Citation Information
[More Information Needed]
### Contributions
Thanks to [@lewtun](https://github.com/lewtun) for adding this dataset. |
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